was read the article
array:22 [ "pii" => "S0210573X23000801" "issn" => "0210573X" "doi" => "10.1016/j.gine.2023.100910" "estado" => "S300" "fechaPublicacion" => "2024-01-01" "aid" => "100910" "copyright" => "Elsevier España, S.L.U.. All rights reserved" "copyrightAnyo" => "2023" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Clin Invest Ginecol Obstet. 2024;51:" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "itemSiguiente" => array:18 [ "pii" => "S0210573X23000837" "issn" => "0210573X" "doi" => "10.1016/j.gine.2023.100913" "estado" => "S300" "fechaPublicacion" => "2024-01-01" "aid" => "100913" "copyright" => "Elsevier España, S.L.U." "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Clin Invest Ginecol Obstet. 2024;51:" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "es" => array:11 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">ORIGINAL</span>" "titulo" => "Secuencia anemia-policitemia en la gestación monocorial. Manejo prenatal y resultados perinatales" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Twin anaemia polycythemia polycythemia sequence in monochorionic gestation. Prenatal management and perinatal outcomes" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "M.J. Cabrero Gañán, M. Turiel Miranda, I. Duyos Mateo, B. Herrero Ruiz, J.L. Bartha Rasero, E. Antolín Alvarado" "autores" => array:6 [ 0 => array:2 [ "nombre" => "M.J." "apellidos" => "Cabrero Gañán" ] 1 => array:2 [ "nombre" => "M." "apellidos" => "Turiel Miranda" ] 2 => array:2 [ "nombre" => "I." "apellidos" => "Duyos Mateo" ] 3 => array:2 [ "nombre" => "B." "apellidos" => "Herrero Ruiz" ] 4 => array:2 [ "nombre" => "J.L." "apellidos" => "Bartha Rasero" ] 5 => array:2 [ "nombre" => "E." "apellidos" => "Antolín Alvarado" ] ] ] ] ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210573X23000837?idApp=UINPBA00004N" "url" => "/0210573X/0000005100000001/v1_202401240434/S0210573X23000837/v1_202401240434/es/main.assets" ] "en" => array:18 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Evaluation of the incorporation of an IOTA-ADNEX model in the discrimination of adnexal masses in our third-level hospital centre, taking into account the menopausal status of patients. Five years of experience" "tieneTextoCompleto" => true "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "N. Elsner Hernández, J.F. De Luis Escudero, L.I. Pérez Méndez, D.R. Báez Quintana, E. Bruno Santana, J.A. Pérez Álvarez, N.R. Sierra Medina, C. Chulilla Pérez, A. Quesada López-Fe" "autores" => array:9 [ 0 => array:4 [ "nombre" => "N." "apellidos" => "Elsner Hernández" "email" => array:1 [ 0 => "nelsher90@gmail.com" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "J.F." "apellidos" => "De Luis Escudero" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "L.I." "apellidos" => "Pérez Méndez" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 3 => array:3 [ "nombre" => "D.R." "apellidos" => "Báez Quintana" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">e</span>" "identificador" => "aff0025" ] ] ] 4 => array:3 [ "nombre" => "E." "apellidos" => "Bruno Santana" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 5 => array:3 [ "nombre" => "J.A." "apellidos" => "Pérez Álvarez" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 6 => array:3 [ "nombre" => "N.R." "apellidos" => "Sierra Medina" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 7 => array:3 [ "nombre" => "C." "apellidos" => "Chulilla Pérez" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 8 => array:3 [ "nombre" => "A." "apellidos" => "Quesada López-Fe" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:5 [ 0 => array:3 [ "entidad" => "Department of Obstetrics and Gynecology, University Hospital Nuestra Señora de Candelaria (HUNSC), Tenerife, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "University Hospital Nuestra Señora de Candelaria (HUNSC), Tenerife, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Division of Clinical Epidemiology and Biostatistics, Research Unit, University Hospital Nuestra Señora de Candelaria (HUNSC) and Primary Care Management, Tenerife, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Networked Biomedical Research Centre (CIBER) of Respiratory Diseases, Carlos III Health Institute, Madrid, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Faculty of Medicine, University of La Laguna, Department of Obstetrics and Gynecology, University Hospital de Canarias (HUC), Tenerife, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Evaluación de la incorporación del IOTA-ADNEX model en la discriminación de las masas anexiales en nuestro centro hospitalario de tercer nivel teniendo en cuenta el estado menopáusico en la predicción de malignidad. Cinco años de experiencia" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 2400 "Ancho" => 3341 "Tamanyo" => 206197 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">ROC curves (AUC). (A) Global, (B) premenopause, and (C) postmenopause.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Ovarian cancer (OC), although rare, is the most aggressive gynecological malignancy.<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">1</span></a> Its incidence in Spain in 2020 is estimated at 13.7/100,000 population/year, 95% CI [12%–15.5%]<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> and mortality is around 5.64/100,000 women/year (rates adjusted to standard European population). In the Canary Islands, the incidence rate adjusted to the standard European population was estimated at 9/100,000/habitants/year for the year 2018.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">3</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The incidence of OC increases with age.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">4–7</span></a> It is known that the incidence of malignant ovarian tumors is different in women who have reached menopause compared to those who have not.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">8</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Usually, OC is diagnosed at a late stage, contributing to its poor prognosis. This, combined with the difficulty in distinguishing between benign and malignant adnexal tumors, results in performing many invasive surgical procedures for malignant tumors that are finally diagnosed as benign results.<a class="elsevierStyleCrossRefs" href="#bib0220"><span class="elsevierStyleSup">9,10</span></a> In the management of an adnexal tumor, a crucial aspect is to establish an initial diagnosis that is as accurate, precise, and exact as possible regarding its nature. The management of a malignant adnexal tumor usually involves surgical removal by a specialized team, whereas benign tumors can be removed or treated conservatively.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">11,12</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Therefore, improving diagnostic methods to distinguish malignant from benign masses in both, the fertile and menopausal stages remain as a challenge.<a class="elsevierStyleCrossRefs" href="#bib0240"><span class="elsevierStyleSup">13,14</span></a> Since its introduction in the mid-1980s, transvaginal ultrasound has become the main imaging modality for the evaluation of adnexal pathology due to its widespread availability, good patient tolerance, and good cost-effectiveness ratio. However, it has not been able to reach its full potential due to the lack of standardization of both the required elements of a transvaginal ultrasound examination and the terms used to describe the findings.</p><p id="par0025" class="elsevierStylePara elsevierViewall">In response to this concern, the International Ovarian Tumor Analysis (IOTA) group developed and validated rules and models based on ultrasound to improve the preoperative characterization of ovarian pathology.<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">15–17</span></a> One of these models is the Assessment of Different NEoplasias in the adneXa (ADNEX) model.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">14,18</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">The ADNEX model is a polynomial model that provides a risk assessment for the different subtypes of adnexal malignancy (borderline cancer, stage I, stage II–IV or metastatic ovarian cancer)<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">14,19</span></a> and estimates the probability that an adnexal tumor is benign or malignant.<a class="elsevierStyleCrossRefs" href="#bib0275"><span class="elsevierStyleSup">20–22</span></a> This model focuses on the evaluation of 9 parameters, 6 ultrasound-based (tumor size, proportion of solid tissue, presence/absence of ascites, presence/absence of papillae and number, presence/absence of more than 10 locules, presence/absence of acoustic shadow), 2 clinical (age, center where the ultrasound is performed), and 1 analytical (serum CA 125 concentration). Depending on the resulting probability, it will guide the specialists in determining the appropriate approach for the management and treatment of the adnexal tumor.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">11</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">If the test result indicates a high probability of malignant adnexal tumor, it will allow for prompt surgical removal by a specialized team. The correct classification of the different subtypes of neoplasia will, in turn, allow for less aggressive surgery to be performed in borderline and stage I tumors, preserving fertility in young women.<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">12,21,23</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">Therefore, the ADNEX model is a diagnostic procedure that is proving to be helpful in pre-surgical decision-making. However, the suggested cut-off points in its design and validation to estimate diagnostic accuracy, where information on sensitivity and specificity is provided, must be recalculated in the clinical environment where they are applied, as recommended by its authors.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">14,24</span></a> At the Ultrasound Unit of the Gynecology and Obstetrics Department of the University Hospital Nuestra Señora de Candelaria (HUNSC), we began using the ADNEX model in 2016. Therefore, in this study, we aimed not only to analyze the performance of the IOTA-ADNEX test after five years of its incorporation into our usual ultrasound tests, but also to estimate the best cut-off point in our setting and to objectify the degree of influence of menopausal status.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Design and patients</span><p id="par0045" class="elsevierStylePara elsevierViewall">A cross-sectional observational study of diagnostic accuracy was applied on patients with the presence of adnexal mass attended by the ultrasound unit of the gynecologic oncology service at a third-level care hospital (Hospital Nuestra Señora de Candelaria) from January 2016 to December 2021. The patients underwent surgical excision by laparoscopy or laparotomy within 180 days after the ultrasound diagnosis, and the tumor was histopathological evaluated (definitive reference diagnosis). These patients were eligible for inclusion. We included consecutive patients with at least one adnexal mass judged not to be a physiological cyst and the data collection from medical records was carried out retrospectively. A total of 573 patients met these criteria. These criteria were selected in order to obtain results that are as comparable as possible with recently published literature.<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">25</span></a> Exclusion criteria were surgical removal of the mass more than 180 days after the ultrasound examination and after undergoing surgery, not having histopathological results during the study period. If more than one mass was detected, we used the mass with the highest risk of malignancy according to the ADNEX model was included in the study (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). This study was approved by the Ethics Committee of the Canary Islands with reference CHUNSC_2020_86.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0050" class="elsevierStylePara elsevierViewall">As this was a study during routine clinical practice, the decision to select surgery or not was made by the gynecologist and shared with the patient after reviewing the complementary tests performed, with no established protocol for this purpose.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Diagnostic methods</span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Preoperative</span><p id="par0055" class="elsevierStylePara elsevierViewall">The ultrasound exams were performed at the Ultrasound Unit of HUNSC by departmental sonographers, all of whom had additional training in gynecologic ultrasound and more than 15 years of experience. Voluson E8 (GE Healthcare Ultrasound, Milwaukee, WI, USA) and/or Toshiba Xsario SSA – 660A (Toshiba Medical Systems Corp., Otawara, Japan) ultrasound machines were used. In most cases, the ultrasound evaluation was performed initially transvaginal, followed by a transabdominal examination. The adnexal masses were described according to the terms and definitions of the IOTA group. Therefore, the diagnosis issued by the sonographers was collected, classifying them as benign tumor, borderline tumor (BOT), stage I, stage II–IV, or metastatic; as well as the risk of malignancy stratification yielded by the IOTA-ADNEX model calculator (<a href="http://www.iotagroup.org/adnexmodel">www.iotagroup.org/adnexmodel</a>) (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). Similarly, the nature of the adnexal mass was systematically evaluated with O-RADS. This data was collected retrospectively from 2020 using the ultrasound images archived in the patient's medical history. This latter evaluation was recoded dichotomously (O-RADS “0–3” and O-RADS “4–5”) for statistical analysis purposes.<a class="elsevierStyleCrossRefs" href="#bib0305"><span class="elsevierStyleSup">26–29</span></a></p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">The results are presented as the total probability of malignancy (TPMAdnex) of the adnexal lesion, which is the main variable for the purpose of the study. However, for descriptive purposes, the risk of each subtype (BOT, stage I, stage II–IV, or metastatic) in the ovary is also estimated.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Standard reference diagnosis</span><p id="par0065" class="elsevierStylePara elsevierViewall">The histopathological diagnosis after surgical excision was the standard reference for evaluating the performance of the aforementioned pre-surgical ultrasound tests. Non-resectable tumors, after clinical evaluation by a trained gynecological oncology surgeon, were defined as stage IIIc or stage IV according to the International Federation of Gynecology and Obstetrics (FIGO).<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">30</span></a> The histological classification followed the International Classification of Ovarian Tumors of the World Health Organization.<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">31</span></a></p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Statistical analysis</span><p id="par0070" class="elsevierStylePara elsevierViewall">First, we performed a descriptive analysis of (1) the demographic and clinical characteristics of the patients, (2) the classification of diagnostic presumption after transvaginal ultrasound performed by expert sonographers, (3) categorization of the adnexal mass by the O-RADS, and (4) the histopathological result.</p><p id="par0075" class="elsevierStylePara elsevierViewall">The area under the receiver-operating-characteristics curve (AUC-ROC) of the ADNEX model for differentiating between benign and malignant adnexal masses at the time of ultrasound examination was calculated using the total probability of malignancy (the sum of four subtypes of malignant ovarian tumors: borderline, stage I cancer, stage II–IV cancer, and secondary metastatic cancer), without taking CA-125 levels into account. The best malignancy threshold, detected by Youden index, was also determined. Sensitivity and specificity were determined for the best threshold, taking into account our clinical environment as recommended by Van Calster,<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">24</span></a> as well as for different cut-off values referenced in the bibliography (5%, 10%, 15%, and 20%).<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">25</span></a></p><p id="par0080" class="elsevierStylePara elsevierViewall">All hypothesis testing was two-tailed with a significance level of 5%, performed using the statistical package SPSS v 26.0. The validity indices were calculated using the epidemiological data tabulation package EPIDAT V 3.0.</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Results</span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Participants</span><p id="par0085" class="elsevierStylePara elsevierViewall">Between January 2016 and December 2021, 573 patients with an average age of 49<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15 years underwent surgery. Just over half of our series (56%) were premenopausal. In <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, we present the demographic and ultrasound characteristics of the overall series and differentiated according to menopausal status, observing a different distribution according to the patient's hormonal status in all analyzed characteristics (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001): (1) the ultrasound suspicion of the four subtypes of malignancy (BOT, stage I, stage II–IV, metastatic) was higher in postmenopausal women. (2) We also highlight a higher percentage in categories 4 and 5 of O-RADS among postmenopausal patients than in premenopausal patients (70.8% vs. 41.5%). (3) Regarding TPMAdnex, we also observed significantly higher values in postmenopausal women.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0090" class="elsevierStylePara elsevierViewall">One hundred and eighty-three patients had positive malignancy histologies, so the prevalence of ovarian cancer, estimated through the histological percentage of malignancy diagnosis, was 31.9%, 95% CI [28.0%–35.8%], significantly lower in pre-menopausal women with 24.7%, 95% CI [19.8%–29.6%], than in postmenopausal women, where it was 41.1%, 95% CI [34.8%–47.4%] (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Histopathological findings</span><p id="par0095" class="elsevierStylePara elsevierViewall">The five most frequent histological subtypes, classified according to the World Health Organization (WHO) and in descending order were among the benign: (1st) teratomas (16.9%), (2nd) endometrioid cyst (16.7%), (3rd) serous cystadenoma (12.6%), serous cystadenofibroma (10%) and tubal tumor-like lesions (8.7%). These findings differed depending on whether the patient was menopausal or not, as shown in <a class="elsevierStyleCrossRefs" href="#tbl0010">Tables 2.1 and 2.2</a>. If we take into account the patient's hormonal status, endometrioid cyst is the most frequent in premenopausal women (24.1%) and less frequent in postmenopausal women (4.7%). The same occurs with teratomas, which are the second most frequent in premenopausal women (23.2%), compared to 6.7% in postmenopausal women. In perimenopause, we find that the most frequent are serous cystadenoma (18.1%) and serous cystadenofibroma (16.8%), which obtained a percentage of 9.1% and 5.8%, respectively, in premenopausal women (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2.1</a>).</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0100" class="elsevierStylePara elsevierViewall">A different distribution was also observed in malignant adnexal tumors depending on whether the patient had a premenopausal status, in which case the two most frequent subtypes were serous and mucinous BOTs with a prevalence of 16.5% each; whereas if the patient presented postmenopause, the most common histological subtype in this condition was high-grade serous carcinoma (29.8%) (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 2.2</a>).</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Malignancy estimation: total probability of malignancy – IOTA-ADNEX model</span><p id="par0105" class="elsevierStylePara elsevierViewall">The TPMAdnex estimate of our patients was significantly lower (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001) in histologically benign adnexal lesions (16.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>20.8) than in malignant ones (71.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>28.2). In the latter, and by histological subtypes: BOT-TPMAdnex 55.5<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>27.7, stage I-TPMAdnex 70.0<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>25.8, stage II–IV-TPMAdnex 89.9<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>19.8, and metastatic-TPMAdnex 70.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>25.1 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001).</p><p id="par0110" class="elsevierStylePara elsevierViewall">In both premenopausal and postmenopausal women, benign tumors always had a lower TPMAdnex than malignant tumors. We observed an average TPMAdnex of 63.9<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>31.3 in malignant lesions of premenopausal women compared to 13.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>20.9 in benign lesions (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). In postmenopausal women, the average TPMAdnex in malignant lesions was 77.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>24.1 vs. 19.8<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>20.1 in benign lesions (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001).</p><p id="par0115" class="elsevierStylePara elsevierViewall">In <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>(A), the performance of the IOTA-ADNEX model is shown for the entire series, with an estimated area under the ROC curve of 0.92 with a 95% CI [0.89–0.94]. When taking menopausal status into account, the area under the ROC curve for premenopausal women is shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>(B), with a value of 0.89 and a 95% CI [0.85–0.94]. For postmenopausal women, the area under the ROC curve is shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>(C), with a value of 0.94 and a 95% CI [0.91–0.97].</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0120" class="elsevierStylePara elsevierViewall">In our setting, the estimated Youden index point of highest sensitivity and specificity joint (91% and 76.4%, respectively) was TPMAdnex of 22.5%. We highlight the difference in these estimators between the premenopausal and postmenopausal groups. In the premenopausal group: sensitivity of 86.1% (95% CI, 85.4%–86.8%) and specificity of 81.3% (95% CI, 85.4%–86.8%), while in the postmenopausal group: sensitivity of 96.1% (95% CI, 95.6%–96.7%), specificity of 68.5% (95% CI, 68.1%–68.8%). <a class="elsevierStyleCrossRef" href="#tbl0020">Table 3</a> also shows these accuracy indices at different cut-off points used in the literature (5%, 10%, 15%, and 20%), differentiated by the hormonal status of the patients.</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><p id="par0125" class="elsevierStylePara elsevierViewall">Therefore, we observed in our study that using the same cutoff point for all patients is not recommended, as it affects the validity of IOTA. The management of an adnexal tumor should be approached as if it were malignant (priority for surgery, biopsy, expert surgeon, etc.) when the IOTA probability of malignancy is >25.75% for postmenopausal patients (sensitivity<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>95.2% and specificity<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>76.6%) and >11.4 for premenopausal patients (S<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>91.1% and E<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>73.4%).</p></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Discussion</span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Summary of main results</span><p id="par0130" class="elsevierStylePara elsevierViewall">We found that the use of the IOTA-ADNEX model, after five years of use, is a reliable diagnostic tool that can be used to rule out ovarian cancer. It would be interesting to take into account the menopausal state of the patient as the accuracy of the test varies.</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Results in the context of published literature</span><p id="par0135" class="elsevierStylePara elsevierViewall">It is known that the incidence of malignant ovarian tumors is different in women who have reached menopause compared to those who did not. Verifying it in our setting where the prevalence of ovarian cancer observed during the study period was 31.9%, which was lower in premenopausal women at 24.7% compared to postmenopausal women where it was 41.1%.</p><p id="par0140" class="elsevierStylePara elsevierViewall">If we refer to the most frequent histological subtypes within our series, we observe that within benign tumors as a whole, teratomas and endometriomas are the most common, which coincides with the distribution of histological types of adnexal masses published by the IOTA group in 2014. This varies if we focus on menopausal patients, with serous cystadenomas being the most common.<a class="elsevierStyleCrossRefs" href="#bib0335"><span class="elsevierStyleSup">32–34</span></a> Regarding the most frequent malignant tumors seen in our series, they are epithelial-derived, serous carcinomas, as described in the literature.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">10</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">The performance of the IOTA-ADNEX model in our population is similar to the performance reported in previous studies, being 0.92.<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">35</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">In our study, the cut-off points for malignancy probability of 15% had a sensitivity of 94%, comparable to the 94.5% found by Van Calster et al. in their pooled analysis.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">14,24</span></a> Our specificity of 68% was lower than theirs, which was 78.7%. Hence, when taking into account menopausal status, which other authors do not reflect in their series, we observe differentiated ranges. Based on these results, in our setting, the cut-off point estimated by the Youden index that has the best combined sensitivity and specificity was 22.5%.</p><p id="par0155" class="elsevierStylePara elsevierViewall">So far, we have not found literature that refers to the variation we have observed in the use of the IOTA-ADNEX model when taking into account the menopausal status of the patient.</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Strengths and weaknesses</span><p id="par0160" class="elsevierStylePara elsevierViewall">The main strength of our study is that the ADNEX model was applied at the time of the ultrasound examination, prior to the histological diagnosis. All cases underwent surgery and histological diagnosis within 180 days. The second strength is that the ultrasound interpreters had experience and were certified by the IOTA group. A limitation of our study may be that the cut-off obtained in our setting of 22.5% may not be generalizable and should be calculated in the clinical setting where it is applied.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">14,24</span></a> Additionally, for the first time, we differentiate the possible variation of accuracy indicators according to hormonal status, suggesting that it be taken into account by gynecologic oncologists.</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Implications for practice and future research</span><p id="par0165" class="elsevierStylePara elsevierViewall">We have estimated that the best cut-off point in our clinical setting for TPMAdnex is 22.5%, which provides us with a better estimate of the sensitivity and specificity of the IOTA-ADNEX model in our setting. However, it is important to take into account the menopausal status of the patient in making clinical decisions, as additional diagnostic tests may be necessary to guide clinical management. Therefore, the ADNEX model is a diagnostic procedure that is proving to assist in preoperative decision-making, although the suggested cutoff points in its design and validation to estimate diagnostic accuracy, where information on sensitivity and specificity is provided, need to be recalculated in the clinical setting where they are applied.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">14,24</span></a></p><p id="par0170" class="elsevierStylePara elsevierViewall">In conclusion, we could raise the following questions: should different cutoff points be chosen according to the menopausal status of the patient? Is it necessary to create a specific follow-up protocol for these patients in postmenopause?</p><p id="par0175" class="elsevierStylePara elsevierViewall">Finally, examining our data, the majority of patients with borderline tumors were premenopausal, perhaps, we have achieved early detection of ovarian cancer in this patient population.</p></span></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Ethical disclosures</span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Protection of human and animal subjects</span><p id="par0180" class="elsevierStylePara elsevierViewall">The authors declare that no experiments were performed on humans or animals for this study.</p></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0155">Confidentiality of data</span><p id="par0185" class="elsevierStylePara elsevierViewall">The authors declare that no patient data appear in this article.</p></span><span id="sec0100" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0160">Right to privacy and informed consent</span><p id="par0190" class="elsevierStylePara elsevierViewall">The authors declare that no patient data appear in this article.</p></span></span><span id="sec0105" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0165">Patient consent</span><p id="par0195" class="elsevierStylePara elsevierViewall">The authors have followed the protocols of our hospital for publication and have obtained consent from our patients for its publication.</p></span><span id="sec0110" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0170">Funding</span><p id="par0200" class="elsevierStylePara elsevierViewall">There is no funding.</p></span><span id="sec0115" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0175">Conflicts of interest</span><p id="par0205" class="elsevierStylePara elsevierViewall">There are no conflicts of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:13 [ 0 => array:3 [ "identificador" => "xres2078829" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1773477" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres2078828" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Método" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1773478" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Design and patients" ] 1 => array:3 [ "identificador" => "sec0020" "titulo" => "Diagnostic methods" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0025" "titulo" => "Preoperative" ] 1 => array:2 [ "identificador" => "sec0030" "titulo" => "Standard reference diagnosis" ] ] ] 2 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 6 => array:3 [ "identificador" => "sec0040" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Participants" ] 1 => array:2 [ "identificador" => "sec0050" "titulo" => "Histopathological findings" ] 2 => array:2 [ "identificador" => "sec0055" "titulo" => "Malignancy estimation: total probability of malignancy – IOTA-ADNEX model" ] ] ] 7 => array:3 [ "identificador" => "sec0060" "titulo" => "Discussion" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0065" "titulo" => "Summary of main results" ] 1 => array:2 [ "identificador" => "sec0070" "titulo" => "Results in the context of published literature" ] 2 => array:2 [ "identificador" => "sec0075" "titulo" => "Strengths and weaknesses" ] 3 => array:2 [ "identificador" => "sec0080" "titulo" => "Implications for practice and future research" ] ] ] 8 => array:3 [ "identificador" => "sec0085" "titulo" => "Ethical disclosures" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0090" "titulo" => "Protection of human and animal subjects" ] 1 => array:2 [ "identificador" => "sec0095" "titulo" => "Confidentiality of data" ] 2 => array:2 [ "identificador" => "sec0100" "titulo" => "Right to privacy and informed consent" ] ] ] 9 => array:2 [ "identificador" => "sec0105" "titulo" => "Patient consent" ] 10 => array:2 [ "identificador" => "sec0110" "titulo" => "Funding" ] 11 => array:2 [ "identificador" => "sec0115" "titulo" => "Conflicts of interest" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-06-02" "fechaAceptado" => "2023-07-15" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1773477" "palabras" => array:7 [ 0 => "Female" 1 => "Gynecology" 2 => "Cross-sectional studies" 3 => "Area under curve" 4 => "Adnexal diseases" 5 => "Ovarian neoplasms" 6 => "Menopause" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1773478" "palabras" => array:7 [ 0 => "Mujer" 1 => "Ginecología" 2 => "Estudio transversal" 3 => "Área bajo la curva" 4 => "Enfermedad anexial" 5 => "Neoplasias ováricas" 6 => "Menopausia" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The objectives were to estimate the performance of the IOTA-ADNEX model test after its incorporation into the ultrasound tests of our third-grade hospital gynecology service, as well as to assess whether its capacity of accuracy is modified when taking into account the patient's menopausal status.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A cross-sectional study was conducted to clinically evaluate the diagnostic performance of the IOTA-ADNEX model test, which was performed between January 2016 and December 2021. The study included 573 women with an adnexal injury who underwent surgical excision within 180 days after ultrasound diagnosis and histological confirmation (gold standard). After the ultrasound exam, the injuries were classified using the ADNEX model. The study estimated the area under the receiver-operating-characteristics curve (AUC) of the ADNEX model for classifying between benign and malignant adnexal masses and compared the performance by menopausal state. Sensitivity and specificity were determined for different cut-off points.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Out of the 573 women, 183 (31.9%) had a malignant tumor. The AUC of the ADNEX model for differentiating between benign and malignant adnexal masses at the time of ultrasound examination was 0.92 and the best malignancy threshold, detected by Youden index, was 22.5%. At this cut-off, the sensitivity of the ADNEX model was 91.8% and the specificity was 76.4%. However, it varies according to menopausal status: in the group of pre-menopausal patient, sensitivity was 86.1% (95% CI, 85.4%–86.8%) and specificity was 81.3% (95% CI, 85.4%–86.8%). In the postmenopausal group, sensitivity was 96.1% (95% CI, 95.6%–96.7%) and specificity was 68.5% (95% CI, 68.1%–68.8%).</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The IOTA-ADNEX model is a reliable diagnostic tool to discard ovarian cancer. However, the accuracy of the test, at the same cut-off point, varies according to the menopausal state of the patient so it may be important to take it into account when taking decisions in clinical practice.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Los objetivos eran estimar el rendimiento del test IOTA ADNEX model después de su incorporación en el estudio ecográfico en nuestro servicio de ginecología, en un hospital de tercer nivel, así como evaluar si su capacidad de precisión se modifica al tener en cuenta el estado menopáusico de la paciente.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Método</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se llevó a cabo un estudio transversal para evaluar clínicamente el rendimiento diagnóstico del test IOTA ADNEX model, el cual se realizó entre enero de 2016 y diciembre de 2021. El estudio incluyó a 573 mujeres con una lesión anexial que se sometieron a tratamiento quirúrgico en un plazo de 180 días después del diagnóstico por ecografía y confirmación histológica (<span class="elsevierStyleItalic">gold standard</span>). Después de realizar la ecografía, las lesiones fueron clasificadas utilizando el modelo ADNEX. El estudio estimó el área bajo la curva (AUC) del modelo ADNEX para diferenciar entre masas anexiales benignas y malignas, y se comparó el rendimiento según el estado menopáusico. Se determinó la sensibilidad y la especificidad para diferentes puntos de corte.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">De las 573 mujeres, 183 (31,9%) tenían un tumor maligno. El AUC del modelo ADNEX para diferenciar entre masas anexiales benignas y malignas en el momento del examen ecográfico fue de 0,92 y el umbral de malignidad óptimo, detectado por el índice de Youden, fue del 22,5%. Con este punto de corte, la sensibilidad (SE) del modelo ADNEX fue del 91,8% y la especificidad (SP) fue del 76,4%. Sin embargo, esto varía según el estado menopáusico: en el grupo de pacientes premenopáusicas, la sensibilidad fue del 86,1% (IC del 95%: 85,4-86,8%) y la especificidad fue del 813% (IC del 95%: 85,4-86,8%). En el grupo de pacientes posmenopáusicas, la sensibilidad fue del 96,1% (IC del 95%: 95,6-96,7%) y la especificidad fue del 68,5% (IC del 95%: 68,1-68,8%).</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">El IOTA ADNEX model es una herramienta de diagnóstico confiable para descartar el cáncer de ovario. Sin embargo, la precisión del test, utilizando el mismo punto de corte, varía según el estado menopáusico de la paciente, por lo que puede ser importante tenerlo en cuenta al tomar decisiones en la práctica clínica.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Método" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1562 "Ancho" => 2508 "Tamanyo" => 183364 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Flowchart of the sample.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1721 "Ancho" => 3341 "Tamanyo" => 297556 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Practical example of the IOTA-ADNEX model when entering ultrasound and demographic data of the patient.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 2400 "Ancho" => 3341 "Tamanyo" => 206197 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">ROC curves (AUC). (A) Global, (B) premenopause, and (C) postmenopause.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">NA: not applicable.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Global<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Premenopausal<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Postmenopausal<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Variables</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">573 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">320 (56) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">253 (44) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Age, years (mean</span><span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">SD)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">49.4<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39.9<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>9.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>9.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Ultrasound diagnosis</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Benign \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">317 (55.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">214 (66.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">103 (40.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Borderline \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">104 (18.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">49 (15.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55 (21.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Stage I \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">81 (14.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33 (10.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48 (19.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Stage II–IV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58 (10.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 (6.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">37 (14.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Metastatic \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (2.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (0.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (4.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">“O-RADS” categories n (5)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001<a class="elsevierStyleCrossRef" href="#tblfn0015">***</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">163 (28.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">123 (38.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">40 (15.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">98 (17.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64 (20.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">34 (13.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">159 (27.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">70 (21.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">89 (35.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">153 (26.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63 (19.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90 (35.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Overall risk of malignancy</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>% ADNEX (mean<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33.8<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>34.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>32.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>35.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Median [P25–P75] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.8 [4.2–63.3] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.6 [3.1–46.7] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31.3 [8.1–78.4] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001<a class="elsevierStyleCrossRef" href="#tblfn0015">***</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Histology, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001<a class="elsevierStyleCrossRef" href="#tblfn0010">**</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Benign \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">390 (68.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">241 (75.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">149 (58.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Malignant \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">183 (31.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">79 (24.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">104 (41.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3442097.png" ] ] ] "notaPie" => array:3 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0005"><span class="elsevierStyleItalic">T</span> student.</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "**" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Pearson's Chi2.</p>" ] 2 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "***" "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Mann–Whitney <span class="elsevierStyleItalic">U</span>.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Demographic, clinical, ultrasound, and histological characteristics, global and according to menopausal status.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2.1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table 2." "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">The values indicated in bold represent the most frequent histology within the menopausal and non-menopausal states.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">WHO code \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Histological subtype \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Global<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Premenopausal<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Postmenopausal<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Benign tumors</span></td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">390 (100) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">241 (61.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">149 (38.2) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Teratoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">66 (16.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56 (<span class="elsevierStyleBold">23.2</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (6.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Endometrioid cyst \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65 (16.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58 (<span class="elsevierStyleBold">24.1</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 (4.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Serous cystadenoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">49 (12.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22 (9.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27 (<span class="elsevierStyleBold">18.1</span>) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Serous cystadenofibroma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39 (10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (5.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25 (<span class="elsevierStyleBold">16.8</span>) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>91 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Tubal tumor-like lesions \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">34 (8.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29 (12) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (3.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mucinous cystadenoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33 (8.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20 (8.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (8.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>84 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Ovarian tumor-like lesions \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33 (8.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (6.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (12.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Fibroma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (5.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (3.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (10.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>23 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Seromucinous cystadenoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (2.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (0.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (5.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mucinous adenofibroma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (2.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (1.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (2.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>36 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Leiomyoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (1.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (1.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>43 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Thecoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (1.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (0.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (2.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Brenner tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (2.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>70 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Struma ovarii \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (1.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Endometrioid adenofibroma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (0.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (2.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Serous adenofibroma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>24 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Seromucinous adenofibroma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Myxoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>45 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sclerosing stromal tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>57 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sertoli-Leydig cell tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3442094.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Tumor histological subtypes according to menopausal status. Benign tumors.</p>" ] ] 5 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 2.2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table 2." "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">The values indicated in bold represent the most frequent histology within the menopausal and non-menopausal states.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">WHO code \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Histological subtype \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Global<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Premenopausal<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Postmenopausal<span class="elsevierStyleItalic">N</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Malignant tumors</span></td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">183 (100) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">79 (43.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">104 (56.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">High-grade serous carcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">40 (21.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (11.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31 (<span class="elsevierStyleBold">29.8</span>) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Borderline mucinous tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 (14.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (<span class="elsevierStyleBold">16.5</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (<span class="elsevierStyleBold">12.5</span>) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Metastatic tumor to ovary \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (12.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (12.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (12.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Borderline serous tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22 (12.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (<span class="elsevierStyleBold">16.5</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (8.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mucinous adenocarcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (7.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (7.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (7.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Endometrioid adenocarcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (7.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (10.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (5.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>22 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Clear cell adenocarcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (4.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (2.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (5.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Borderline seromucinous tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (4.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (3.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (5.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Borderline endometrioid tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (1.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (2.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>32 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Carcinosarcoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Leiomyosarcoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Immature teratoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Dysgerminoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yolk sac tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Tubal high-grade serous carcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Low-grade serous carcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Seromucinous carcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Borderline clear cell tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>27 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Borderline Brenner tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Undifferentiated carcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Adenosarcoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>52 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Granulosa cell tumor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sertoli-Leydig tumor w. moderate differentiation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>59 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sertoli-Leydig tumor w. poor differentiation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Malignant teratoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Small cell carcinoma \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3442096.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Tumor histological subtype according to menopausal status. Malignant tumors.</p>" ] ] 6 => array:8 [ "identificador" => "tbl0020" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at4" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Malignancy risk threshold \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Sensitivity%, 95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Specificity%, 95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">≥5% all</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96.2 (95.9–96.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">40.8 (40.6–41.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Premenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">93.7 (93.0–94.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52.3 (52.0–52.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Postmenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">98.1 (97.6–98.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22.2 (21.8–22.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">≥10% all</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.5 (94.2–94.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.0 (60.9–61.2) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Premenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91.1 (90.5–91.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72.2 (72.0–72.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Postmenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">97.1 (96.6–97.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43.0 (42.6–43.3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">≥15% all</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.0 (93.7–94.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">67.4 (67.3–67.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Premenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">89.9 (89.2–90.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">75.9 (75.7–76.2) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Postmenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">97.1 (96.6–97.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.7 (53.3–54.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">≥20% all</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">93.0 (92.6–93.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">74.1 (73.9–74.3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Premenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">87.3 (86.7–88.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">80.1 (80.3–80.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Postmenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">97.1 (96.6–97.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.8 (63.4–64.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">≥22.5% all</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91.8 (91.5–92.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">76.4 (76.3–76.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Premenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86.1 (85.4–86.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">81.3 (81.1–81.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Postmenopausal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96.1 (95.6–96.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">68.5 (68.1–68.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3442095.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Evaluation of the IOTA-ADNEX model at different cut-off points of total malignancy risk. Global and according to menopausal status.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:35 [ 0 => array:3 [ "identificador" => "bib0180" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:1 [ "titulo" => "Cancer statistics review" ] ] "host" => array:2 [ 0 => array:1 [ "Libro" => array:2 [ "fecha" => "2020" "editorial" => "National Cancer Institute" ] ] 1 => array:1 [ "WWW" => array:1 [ "link" => "https://Seer.Cancer.Gov/Csr/1975_2017/" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0185" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:1 [ "titulo" => "Estimaciones de la incidencia del cáncer en España" ] ] "host" => array:2 [ 0 => array:1 [ "Libro" => array:2 [ "fecha" => "2022" "editorial" => "Red Española de Registros de Cáncer" ] ] 1 => array:1 [ "WWW" => array:1 [ "link" => "https://seom.org/seomcms/images/stories/recursos/Cifras_del_cancer_2020.pdf" ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0190" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:1 [ "titulo" => "Estimaciones de la incidencia de cáncer. Canarias" ] ] "host" => array:2 [ 0 => array:1 [ "Libro" => array:2 [ "fecha" => "2018" "editorial" => "Red Española de Registros de Cáncer" ] ] 1 => array:1 [ "WWW" => array:1 [ "link" => "https://www3.gobiernodecanarias.org/sanidad/scs/content/8e1d1c9c-43fd-11e9-af3a-bd8e6246c9be/Estimacion_Incidencia_Cancer_Canarias2018.pdf" ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0195" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Risk factors for epithelial ovarian cancer by histologic subtype" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "M.A. Gates" 1 => "B.A. Rosner" 2 => "J.L. Hecht" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1093/aje/kwp314" "Revista" => array:3 [ "tituloSerie" => "Am J Epidemiol" "fecha" => "2010" "volumen" => "171" ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0200" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Ovarian cancer detection in average-risk women: classic- versus nonclassic-appearing adnexal lesions at US" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Gupta" 1 => "P. Jha" 2 => "T.M. Baran" 3 => "K.E. Maturen" 4 => "K. Patel-Lippmann" 5 => "H.M. Zafar" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1148/radiol.212338" "Revista" => array:3 [ "tituloSerie" => "Radiology" "fecha" => "2022" "volumen" => "303" ] ] ] ] ] ] 5 => array:3 [ "identificador" => "bib0205" "etiqueta" => "6" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The impact of comorbidity and stage on ovarian cancer mortality: a nationwide Danish cohort study" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "M.S. Tetsche" 1 => "C. Dethlefsen" 2 => "L. Pedersen" 3 => "H.T. Sorensen" 4 => "M. Norgaard" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1186/1471-2407-8-31" "Revista" => array:3 [ "tituloSerie" => "BMC Cancer" "fecha" => "2008" "volumen" => "8" ] ] ] ] ] ] 6 => array:3 [ "identificador" => "bib0210" "etiqueta" => "7" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Age-dependent differences in borderline ovarian tumours (BOT) regarding clinical characteristics and outcome: results from a sub-analysis of the Arbeitsgemeinschaft Gynaekologische Onkologie (AGO) ROBOT study" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "F. Trillsch" 1 => "S. Mahner" 2 => "L. Woelber" 3 => "E. Vettorazzi" 4 => "A. Reuss" 5 => "N. Ewald-Riegler" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1093/annonc/mdu119" "Revista" => array:3 [ "tituloSerie" => "Ann Oncol" "fecha" => "2014" "volumen" => "25" ] ] ] ] ] ] 7 => array:3 [ "identificador" => "bib0215" "etiqueta" => "8" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Indeterminate ovarian mass at US: incremental value of second imaging test for characterization-meta-analysis and Bayesian analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "K. Kinkel" 1 => "Y. Lu" 2 => "A. Mehdizade" 3 => "M.F. Pelte" 4 => "H. Hricak" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1148/radiol.2361041618" "Revista" => array:3 [ "tituloSerie" => "Radiology" "fecha" => "2005" "volumen" => "236" ] ] ] ] ] ] 8 => array:3 [ "identificador" => "bib0220" "etiqueta" => "9" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Ovarian cancer screening in the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial: findings from the initial screen of a randomized trial" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S.S. Buys" 1 => "E. Partridge" 2 => "M.H. Greene" 3 => "P.C. Prorok" 4 => "D. Reding" 5 => "T.L. Riley" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ajog.2005.05.005" "Revista" => array:3 [ "tituloSerie" => "Am J Obstet Gynecol" "fecha" => "2005" "volumen" => "193" ] ] ] ] ] ] 9 => array:3 [ "identificador" => "bib0225" "etiqueta" => "10" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Epidemiology of epithelial ovarian cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "P.M. Webb" 1 => "S.J. Jordan" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.bpobgyn.2016.08.006" "Revista" => array:3 [ "tituloSerie" => "Best Pract Res Clin Obstet Gynaecol" "fecha" => "2017" "volumen" => "41" ] ] ] ] ] ] 10 => array:3 [ "identificador" => "bib0230" "etiqueta" => "11" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Risk of complications in patients with conservatively managed ovarian tumours (IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "W. Froyman" 1 => "C. Landolfo" 2 => "B. De Cock" 3 => "L. Wynants" 4 => "P. Sladkevicius" 5 => "A.C. Testa" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/S1470-2045(18)30837-4" "Revista" => array:6 [ "tituloSerie" => "Lancet Oncol" "fecha" => "2019" "volumen" => "20" "paginaInicial" => "448" "paginaFinal" => "458" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/30737137" "web" => "Medline" ] ] ] ] ] ] ] ] 11 => array:3 [ "identificador" => "bib0235" "etiqueta" => "12" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The role of laparoscopy in ovarian tumors of low malignant potential and early-stage ovarian cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "E. Vaisbuch" 1 => "R. Dgani" 2 => "A. Ben-Arie" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1097/01.ogx.0000161373.94922.33" "Revista" => array:2 [ "tituloSerie" => "Obstet Gynecol Surv" "fecha" => "2005" ] ] ] ] ] ] 12 => array:3 [ "identificador" => "bib0240" "etiqueta" => "13" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Evaluación ecográfica de las masas anexiales" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "J.L.A. Zambrano" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.20960/j.pog.00108" "Revista" => array:3 [ "tituloSerie" => "Prog Obstet Ginecol" "fecha" => "2018" "volumen" => "61" ] ] ] ] ] ] 13 => array:3 [ "identificador" => "bib0245" "etiqueta" => "14" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "B. Van Calster" 1 => "K. Van Hoorde" 2 => "L. Valentin" 3 => "A.C. Testa" 4 => "D. Fischerova" 5 => "C. Van Holsbeke" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1136/bmj.g5920" "Revista" => array:5 [ "tituloSerie" => "BMJ" "fecha" => "2014" "volumen" => "349" "paginaInicial" => "g5920" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25320247" "web" => "Medline" ] ] ] ] ] ] ] ] 14 => array:3 [ "identificador" => "bib0250" "etiqueta" => "15" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Subjective ultrasound assessment, the ADNEX model and ultrasound-guided tru-cut biopsy to differentiate disseminated primary ovarian cancer from metastatic non-ovarian cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "E. Epstein" 1 => "B. Van Calster" 2 => "D. Timmerman" 3 => "S. Nikman" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/uog.14892" "Revista" => array:3 [ "tituloSerie" => "Ultrasound Obstet Gynecol" "fecha" => "2016" "volumen" => "47" ] ] ] ] ] ] 15 => array:3 [ "identificador" => "bib0255" "etiqueta" => "16" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Towards an evidence-based approach for diagnosis and management of adnexal masses: findings of the International Ovarian Tumour Analysis (IOTA) studies" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "J. Kaijser" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Facts Views Vis Obgyn" "fecha" => "2015" "volumen" => "7" "paginaInicial" => "42" "paginaFinal" => "59" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25897371" "web" => "Medline" ] ] ] ] ] ] ] ] 16 => array:3 [ "identificador" => "bib0260" "etiqueta" => "17" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D. Timmerman" 1 => "B. Van Calster" 2 => "A. Testa" 3 => "L. Savelli" 4 => "D. Fischerova" 5 => "W. Froyman" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ajog.2016.01.007" "Revista" => array:6 [ "tituloSerie" => "Am J Obstet Gynecol" "fecha" => "2016" "volumen" => "214" "paginaInicial" => "424" "paginaFinal" => "437" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26800772" "web" => "Medline" ] ] ] ] ] ] ] ] 17 => array:3 [ "identificador" => "bib0265" "etiqueta" => "18" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. Kaijser" 1 => "A. Sayasneh" 2 => "K. Van Hoorde" 3 => "S. Ghaem-Maghami" 4 => "T. Bourne" 5 => "D. Timmerman" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1093/humupd/dmt059" "Revista" => array:6 [ "tituloSerie" => "Hum Reprod Update" "fecha" => "2014" "volumen" => "20" "paginaInicial" => "449" "paginaFinal" => "462" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24327552" "web" => "Medline" ] ] ] ] ] ] ] ] 18 => array:3 [ "identificador" => "bib0270" "etiqueta" => "19" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Before surgery predictability of malignant ovarian tumors based on ADNEX model and its use in clinical practice" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "E. Joyeux" 1 => "T. Miras" 2 => "I. Masquin" 3 => "P.-E. Duglet" 4 => "K. Astruc" 5 => "S. Douvier" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.gyobfe.2016.07.007" "Revista" => array:6 [ "tituloSerie" => "Gynecol Obstet Fertil" "fecha" => "2016" "volumen" => "44" "paginaInicial" => "557" "paginaFinal" => "564" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27568408" "web" => "Medline" ] ] ] ] ] ] ] ] 19 => array:3 [ "identificador" => "bib0275" "etiqueta" => "20" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "External validation of the IOTA ADNEX model performed by two independent gynecologic centers" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S. Szubert" 1 => "A. Wojtowicz" 2 => "R. Moszynski" 3 => "P. Zywica" 4 => "K. Dyczkowski" 5 => "A. Stachowiak" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ygyno.2016.06.020" "Revista" => array:6 [ "tituloSerie" => "Gynecol Oncol" "fecha" => "2016" "volumen" => "142" "paginaInicial" => "490" "paginaFinal" => "495" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27374142" "web" => "Medline" ] ] ] ] ] ] ] ] 20 => array:3 [ "identificador" => "bib0280" "etiqueta" => "21" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Sayasneh" 1 => "L. Ferrara" 2 => "B. De Cock" 3 => "S. Saso" 4 => "M. Al-Memar" 5 => "S. Johnson" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1038/bjc.2016.227" "Revista" => array:6 [ "tituloSerie" => "Br J Cancer" "fecha" => "2016" "volumen" => "115" "paginaInicial" => "542" "paginaFinal" => "548" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27482647" "web" => "Medline" ] ] ] ] ] ] ] ] 21 => array:3 [ "identificador" => "bib0285" "etiqueta" => "22" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "E.M.J. Meys" 1 => "L.S. Jeelof" 2 => "N.M.J. Achten" 3 => "B.F.M. Slangen" 4 => "S. Lambrechts" 5 => "R.F.P.M. Kruitwagen" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/uog.17225" "Revista" => array:6 [ "tituloSerie" => "Ultrasound Obstet Gynecol" "fecha" => "2017" "volumen" => "49" "paginaInicial" => "784" "paginaFinal" => "792" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27514486" "web" => "Medline" ] ] ] ] ] ] ] ] 22 => array:3 [ "identificador" => "bib0290" "etiqueta" => "23" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Adolescent ovarian tumours: a gynecologist's dilemma" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "S. Tanksale" 1 => "K. Bendre" 2 => "G. Niyogi" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.18203/2320-1770.ijrcog20150102" "Revista" => array:5 [ "tituloSerie" => "Int J Reprod Contracept Obstet Gynecol" "fecha" => "2015" "volumen" => "4" "paginaInicial" => "833" "paginaFinal" => "836" ] ] ] ] ] ] 23 => array:3 [ "identificador" => "bib0295" "etiqueta" => "24" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "B. Van Calster" 1 => "K. Van Hoorde" 2 => "W. Froyman" 3 => "J. Kaijser" 4 => "L. Wynants" 5 => "C. Landolfo" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Facts Views Vis Obgyn" "fecha" => "2015" "volumen" => "7" "paginaInicial" => "32" "paginaFinal" => "41" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25897370" "web" => "Medline" ] ] ] ] ] ] ] ] 24 => array:3 [ "identificador" => "bib0300" "etiqueta" => "25" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Performance of IOTA simple rules, simple rules risk assessment, ADNEX model and O-RADS in differentiating between benign and malignant adnexal lesions in North American women" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "A.K. Hiett" 1 => "J.D. Sonek" 2 => "M. Guy" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/uog.24777" "Revista" => array:6 [ "tituloSerie" => "Ultrasound Obstet Gynecol" "fecha" => "2022" "volumen" => "59" "paginaInicial" => "668" "paginaFinal" => "676" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/34533862" "web" => "Medline" ] ] ] ] ] ] ] ] 25 => array:3 [ "identificador" => "bib0305" "etiqueta" => "26" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "External validation of the ovarian-adnexal reporting and data system (O-RADS) lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to stratify ovarian tumors into O-RADS risk groups" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S. Timmerman" 1 => "L. Valentin" 2 => "J. Ceusters" 3 => "A.C. Testa" 4 => "C. Landolfo" 5 => "P. Sladkevicius" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1001/jamaoncol.2022.5969" "Revista" => array:6 [ "tituloSerie" => "JAMA Oncol" "fecha" => "2023" "volumen" => "9" "paginaInicial" => "225" "paginaFinal" => "233" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/36520422" "web" => "Medline" ] ] ] ] ] ] ] ] 26 => array:3 [ "identificador" => "bib0310" "etiqueta" => "27" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Diagnostic performance of the ovarian-adnexal reporting and data system (O-RADS) ultrasound risk score in women in the United States" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "P. Jha" 1 => "A. Gupta" 2 => "T.M. Baran" 3 => "K.E. Maturen" 4 => "K. Patel-Lippmann" 5 => "H.M. Zafar" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1001/jamanetworkopen.2022.16370" "Revista" => array:5 [ "tituloSerie" => "JAMA Netw Open" "fecha" => "2022" "volumen" => "5" "paginaInicial" => "e2216370" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/35679042" "web" => "Medline" ] ] ] ] ] ] ] ] 27 => array:3 [ "identificador" => "bib0315" "etiqueta" => "28" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): analysis on 1054 adnexal masses" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "L. Cao" 1 => "M. Wei" 2 => "Y. Liu" 3 => "J. Fu" 4 => "H. Zhang" 5 => "J. Huang" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ygyno.2021.04.031" "Revista" => array:6 [ "tituloSerie" => "Gynecol Oncol" "fecha" => "2021" "volumen" => "162" "paginaInicial" => "107" "paginaFinal" => "112" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/33966893" "web" => "Medline" ] ] ] ] ] ] ] ] 28 => array:3 [ "identificador" => "bib0320" "etiqueta" => "29" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "O-RADS US risk stratification and management system: a consensus guideline from the ACR ovarian-adnexal reporting and data system committee" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "R.F. Andreotti" 1 => "D. Timmerman" 2 => "L.M. Strachowski" 3 => "W. Froyman" 4 => "B.R. Benacerraf" 5 => "G.L. Bennett" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1148/radiol.2019191150" "Revista" => array:6 [ "tituloSerie" => "Radiology" "fecha" => "2020" "volumen" => "294" "paginaInicial" => "168" "paginaFinal" => "185" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/31687921" "web" => "Medline" ] ] ] ] ] ] ] ] 29 => array:3 [ "identificador" => "bib0325" "etiqueta" => "30" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Staging classification for cancer of the ovary, fallopian tube, and peritoneum" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "J. Prat" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ijgo.2013.10.001" "Revista" => array:5 [ "tituloSerie" => "Int J Gynecol Obstet" "fecha" => "2014" "volumen" => "124" "paginaInicial" => "1" "paginaFinal" => "5" ] ] ] ] ] ] 30 => array:3 [ "identificador" => "bib0330" "etiqueta" => "31" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "World Health Organisation classification of tumours of the female reproductive organs" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "C. Scheuer" 1 => "M.L. Carcangiu" 2 => "C.S. Herrington" 3 => "R.H. Young" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:3 [ "fecha" => "2014" "paginaInicial" => "11" "paginaFinal" => "83" ] ] ] ] ] ] 31 => array:3 [ "identificador" => "bib0335" "etiqueta" => "32" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Natural history of sonographically detected simple unilocular adnexal cysts in asymptomatic postmenopausal women" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "G. Castillo" 1 => "J.L. Alcázar" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ygyno.2003.11.029" "Revista" => array:6 [ "tituloSerie" => "Gynecol Oncol" "fecha" => "2004" "volumen" => "92" "paginaInicial" => "965" "paginaFinal" => "969" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/14984967" "web" => "Medline" ] ] ] ] ] ] ] ] 32 => array:3 [ "identificador" => "bib0340" "etiqueta" => "33" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Frequency and type of adnexal lesions in autopsy material from postmenopausal women: ultrasound study with histological correlation" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "L. Valentin" 1 => "L. Skoog" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/uog.212" "Revista" => array:6 [ "tituloSerie" => "Ultrasound Obstet Gynecol" "fecha" => "2003" "volumen" => "22" "paginaInicial" => "284" "paginaFinal" => "289" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/12942502" "web" => "Medline" ] ] ] ] ] ] ] ] 33 => array:3 [ "identificador" => "bib0345" "etiqueta" => "34" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Prevalence and histologic diagnosis of adnexal cysts in postmenopausal women: an autopsy study" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "A. Dørum" 1 => "G.P. Blom" 2 => "E. Ekerhovd" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ajog.2004.07.038" "Revista" => array:5 [ "tituloSerie" => "Am J Obstet Gynecol" "fecha" => "2005" "volumen" => "192" "paginaInicial" => "48" "paginaFinal" => "54" ] ] ] ] ] ] 34 => array:3 [ "identificador" => "bib0350" "etiqueta" => "35" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "K.G. Araujo" 1 => "R.M. Jales" 2 => "P.N. Pereira" 3 => "A. Yoshida" 4 => "L. de Angelo Andrade" 5 => "L.O. Sarian" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/uog.15963" "Revista" => array:6 [ "tituloSerie" => "Ultrasound Obstet Gynecol" "fecha" => "2017" "volumen" => "49" "paginaInicial" => "778" "paginaFinal" => "783" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27194129" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] ] "idiomaDefecto" => "en" "url" => "/0210573X/0000005100000001/v1_202401240434/S0210573X23000801/v1_202401240434/en/main.assets" "Apartado" => array:4 [ "identificador" => "76032" "tipo" => "SECCION" "es" => array:2 [ "titulo" => "Original" "idiomaDefecto" => true ] "idiomaDefecto" => "es" ] "PDF" => "https://static.elsevier.es/multimedia/0210573X/0000005100000001/v1_202401240434/S0210573X23000801/v1_202401240434/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210573X23000801?idApp=UINPBA00004N" ]
Year/Month | Html | Total | |
---|---|---|---|
2024 November | 2 | 2 | 4 |
2024 October | 11 | 9 | 20 |
2024 September | 8 | 8 | 16 |
2024 August | 11 | 9 | 20 |
2024 July | 11 | 10 | 21 |
2024 June | 10 | 11 | 21 |
2024 May | 10 | 5 | 15 |
2024 April | 9 | 6 | 15 |
2024 March | 14 | 9 | 23 |
2024 February | 9 | 4 | 13 |
2024 January | 20 | 6 | 26 |
2023 December | 11 | 1 | 12 |
2023 November | 9 | 1 | 10 |
2023 October | 27 | 1 | 28 |
2023 September | 10 | 0 | 10 |
2023 August | 5 | 4 | 9 |