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array:22 [ "pii" => "S0210573X24000637" "issn" => "0210573X" "doi" => "10.1016/j.gine.2024.101000" "estado" => "S250" "fechaPublicacion" => "2025-01-01" "aid" => "101000" "copyright" => "Elsevier España, S.L.U.. All rights reserved" "copyrightAnyo" => "2024" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "itemSiguiente" => array:17 [ "pii" => "S0210573X24000571" "issn" => "0210573X" "doi" => "10.1016/j.gine.2024.100994" "estado" => "S250" "fechaPublicacion" => "2025-01-01" "aid" => "100994" "copyright" => "Elsevier España, S.L.U." "documento" => "simple-article" "crossmark" => 1 "subdocumento" => "crp" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "es" => array:12 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Caso clínico</span>" "titulo" => "Restos ovulares y placentarios (ROP) y nódulo del lecho placentario (NLP) en un istmocele tras un parto vaginal: diagnóstico y corrección laparoscópica" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Retained products of conception (RPOC) and placental site nodule (PSN) in an isthmocele after a vaginal delivery: Diagnosis and laparoscopic repair" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figura 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1306 "Ancho" => 862 "Tamanyo" => 248910 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Hallazgos histológicos compatibles con nódulo del lecho placentario.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "L. Moreno Leñero, A. Morales Vicente, S. Moner Marín, P. Millares Rubio, N. Santonja López, Y. García Sánchez, J. Gilabert Estellés" "autores" => array:7 [ 0 => array:2 [ "nombre" => "L." "apellidos" => "Moreno Leñero" ] 1 => array:2 [ "nombre" => "A." "apellidos" => "Morales Vicente" ] 2 => array:2 [ "nombre" => "S." "apellidos" => "Moner Marín" ] 3 => array:2 [ "nombre" => "P." "apellidos" => "Millares Rubio" ] 4 => array:2 [ "nombre" => "N." "apellidos" => "Santonja López" ] 5 => array:2 [ "nombre" => "Y." "apellidos" => "García Sánchez" ] 6 => array:2 [ "nombre" => "J." "apellidos" => "Gilabert Estellés" ] ] ] ] ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210573X24000571?idApp=UINPBA00004N" "url" => "/0210573X/0000005200000001/v12_202410280410/S0210573X24000571/v12_202410280410/es/main.assets" ] "itemAnterior" => array:16 [ "pii" => "S0210573X24000595" "issn" => "0210573X" "doi" => "10.1016/j.gine.2024.100996" "estado" => "S250" "fechaPublicacion" => "2025-01-01" "aid" => "100996" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "en" => array:12 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Perinatal outcomes in pregnant women over 45 years old: Singleton or multiple pregnancy?" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Resultados perinatales en gestantes mayores de 45 años: ¿embarazo único o múltiple?" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1361 "Ancho" => 2458 "Tamanyo" => 133861 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Perinatal outcomes (%).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "S. Ruiz-Martínez, C. Sánchez Cabezas, N. Mateos Canals, N. Martínez-Sánchez, M. Muner, E. Martin-Boado, M. Calvo, J.L. Bartha, M. De la Calle" "autores" => array:9 [ 0 => array:2 [ "nombre" => "S." "apellidos" => "Ruiz-Martínez" ] 1 => array:2 [ "nombre" => "C." "apellidos" => "Sánchez Cabezas" ] 2 => array:2 [ "nombre" => "N." "apellidos" => "Mateos Canals" ] 3 => array:2 [ "nombre" => "N." "apellidos" => "Martínez-Sánchez" ] 4 => array:2 [ "nombre" => "M." "apellidos" => "Muner" ] 5 => array:2 [ "nombre" => "E." "apellidos" => "Martin-Boado" ] 6 => array:2 [ "nombre" => "M." "apellidos" => "Calvo" ] 7 => array:2 [ "nombre" => "J.L." "apellidos" => "Bartha" ] 8 => array:2 [ "nombre" => "M." "apellidos" => "De la Calle" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210573X24000595?idApp=UINPBA00004N" "url" => "/0210573X/0000005200000001/v12_202410280410/S0210573X24000595/v12_202410280410/en/main.assets" ] "en" => array:18 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Diagnostic rentability of IOTA models for differentiating between benign and malignant complex adnexal masses" "tieneTextoCompleto" => true "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "A. Rodríguez Pérez, A. Caruso, M. Pantoja Garrido, I. Rodríguez Jiménez, A. Polo Velasco, J.J. Fernández Alba" "autores" => array:6 [ 0 => array:4 [ "nombre" => "A." "apellidos" => "Rodríguez Pérez" "email" => array:1 [ 0 => "alba.rodriguezprz@gmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "A." "apellidos" => "Caruso" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "M." "apellidos" => "Pantoja Garrido" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 3 => array:3 [ "nombre" => "I." "apellidos" => "Rodríguez Jiménez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 4 => array:3 [ "nombre" => "A." "apellidos" => "Polo Velasco" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 5 => array:3 [ "nombre" => "J.J." "apellidos" => "Fernández Alba" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Department of Gynecology Hospital San Juan de Dios del Aljarafe, Bormujos, Seville, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Obstetrics and Gynecology Hospital Universitario Virgen Macarena, Seville, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Department of Obstetrics and Gynecology Hospital Universitario de Puerto Real, Cádiz, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Rentabilidad diagnóstica de los modelos IOTA para diferenciar entre masas anexiales complejas benignas y malignas" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 956 "Ancho" => 1675 "Tamanyo" => 85911 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">PR curve for LR1–LR2 superimposed, the green color represents LR1 model and the red one represents LR2 model. The curve of LR1 model is higher than LR2 model.</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 tumors represent the most common type of adnexal mass.<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">1</span></a> Adnexal mass are any solid or cystic formation dependent on the ovary, fallopian tube or surrounding connective tissues.<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">1</span></a> Fortunately, most of them are non-malignant/cancerous.</p><p id="par0010" class="elsevierStylePara elsevierViewall">The probability of developing an ovarian tumor throughout a Spanish woman's lifetime is 1.84% according to the Spanish Network of Cancer Registries (REDECAN). However, ovarian cancer is the most lethal neoplasm among gynecological tumors, with a 5-year relative survival rate of 37%<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">2</span></a> and accounting for 4.7% of cancer mortality in women in Spain during 2020.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">3</span></a> Ovarian cancer is the fifth most common tumor in Spain<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">3</span></a> and ranks second among gynecological tumors, behind endometrial cancer. Diagnosis usually occurs at advanced stages in 70–75% of cases, as initial stages, where the tumor is confined to the ovary, usually lack symptoms or have nonspecific ones. This translates into a worse prognosis in affected individuals and poor treatment response. Survival rates are higher than 70% for initial stages (I, II) compared to 20–40% for advanced stages (III, IV).<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">4</span></a> Although diagnosis of initial stages of ovarian cancer would benefit women affected by it, the fact that most of adnexal masses are non-malignant/cancerous makes complex to rise awareness into the correct classification of these to avoid unnecessary morbidity and costs from an unjustified intervention.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">5</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Adnexal tumors are a common issue in daily gynecology practice. The primary objective when evaluating these formations is to determine their potential malignancy to provide the most appropriate care for each patient. Various strategies have been introduced to optimize the preoperative evaluation of adnexal masses, allowing for the categorization of a tumor as “highly suspicious.” Regarding the use of complementary tests, transvaginal ultrasound is the main tool for evaluating these masses.<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">2</span></a> The major contribution in this diagnosis lies in the educated, yet subjective assessment of an experienced sonographer.<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">5–8</span></a> Thus, the diagnostic performance of ultrasound have been optimized through the development of more reproducible strategies based on classification systems<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">9</span></a> and logistic regression models<a class="elsevierStyleCrossRefs" href="#bib0185"><span class="elsevierStyleSup">10,11</span></a> proposed by the “International Ovarian Tumor Analysis” (IOTA) group.</p><p id="par0020" class="elsevierStylePara elsevierViewall">Our study aimed to evaluate the diagnostic performance of these indices using histological analysis as a reference. The goal was to determine which of the four preoperative evaluation indices for complex adnexal masses proposed by IOTA offers the highest diagnostic accuracy in our setting: Assessment of Different NEoplasias in the adneXa (ADNEX), Multivariable logistic regression model 1 (LR1), Multivariable logistic regression model 2 (LR2) and simple rules.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">This is a cross-sectional observational study of diagnostic accuracy. The study was conducted in the Obstetrics and Gynecology Service of the University Hospital Virgen Macarena of Seville between January 2017 and December 2022.</p><p id="par0030" class="elsevierStylePara elsevierViewall">The sample size of this study was determined assuming a diagnostic accuracy of 80% for LR1 and LR2, 65% for simple rules and 90% for ADNEX, with a 95% confidence level, it was calculated that 202 women would be required to achieve a statistical power of 80% (calculations performed using EPIDAT 3.1 software). This estimation refers to the number of women with pathology required. Assuming that 7 out of every 10 women included in the study will indeed have cancer, the total sample size would be 288 women.</p><p id="par0035" class="elsevierStylePara elsevierViewall">Patients included in the study were assessed by an expert ultrasound operator who produced a report based on the Gynecology Imaging Reporting and Data System (GI-RADS), proposed by Amor et al. in 2009.<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">12</span></a> This is a reporting system based on ultrasound findings, and estimates risk of malignancy for a given adnexal mass, ranging from GI-RADS 1: definitively benign, to GI-RADS 5: very probably malignant. Patients with complex adnexal mass classified as intermediate or high suspicion of malignancy: GIRADS 4 and 5 (probability of malignancy 5–20% and >20% respectively) were recommended for surgical intervention and included in the study.</p><p id="par0040" class="elsevierStylePara elsevierViewall">The probability of malignancy within an adnexal mass was based on a series of ultrasound items and patient personal characteristics estimated by using four different IOTA models: logistic regression model 1(LR1) and logistic regression model 2 (LR2), Assessment of Different Neoplasias in the adnexa (ADNEX model) and simple rules.</p><p id="par0045" class="elsevierStylePara elsevierViewall">The simple rules consist of five ultrasound features of malignancy (M) and five of benignity (B). The M-Rules are: M1: Irregular solid tumor, M2: Ascites, M3: At least four papillary structures, M4: Multilocular solid tumor, diameter<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>100<span class="elsevierStyleHsp" style=""></span>mm, M5: Intense blood flow. Doppler score color 4; on the other hand the B-Rules are: B1: Unilocular cyst, B2: Solid component, <7<span class="elsevierStyleHsp" style=""></span>mm, B3: Accoustic shadow, B4: Multilocular smooth tumor, diameter<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>100<span class="elsevierStyleHsp" style=""></span>mm, B5: Absence of blood flow. Doppler score color 1. A tumor is classified as malignant if it presents at least one M feature and none of the B features, on the other side we consider benign if it presents none M features and at least one B feature.<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">9</span></a> Lesions with features of both categories or none will be classified as “inconclusive or unclassifiable”.</p><p id="par0050" class="elsevierStylePara elsevierViewall">Twelve variables were used for the LR1 calculation<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">10</span></a>: 1. Personal history of ovarian cancer; 2. Current hormonal therapy use; 3. Patient age (years); 4. Maximum diameter of the lesion (mm); 5. Evidence of pain during mass examination; 6. Presence of ascites; 7. Presence of blood flow within a solid papillary projection; 8. Purely solid tumor; 9. Maximum diameter of the largest solid component (expressed in mm, but without an increase<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>50<span class="elsevierStyleHsp" style=""></span>mm); 10. Internal irregular cyst walls; 11. Presence of acoustic shadows; 12. Score Doppler color.</p><p id="par0055" class="elsevierStylePara elsevierViewall">LR2 was calculated based on six of the above variables: 3. Patient age (years); 6. Presence of ascites; 7. Presence of blood flow within a solid papillary projection; 9. Maximum diameter of the largest solid component (expressed in mm, but without an increase<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>50<span class="elsevierStyleHsp" style=""></span>mm); 10. Internal irregular cyst walls; 11. Presence of acoustic shadows.</p><p id="par0060" class="elsevierStylePara elsevierViewall">Both offer similar diagnostic performance as to sensitivity, specificity focusing on discrimination between stage I primary invasive ovarian malignancies and benign tumors.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">13</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">On the other hand, the ADNEX model (Assessment of Different Neoplasias in the adneXa) provides an absolute risk estimate for five different types of adnexal pathology based on 9 variables: ultrasound, serum Ca125 level, and patient age.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">11</span></a> To date, no proposed model has demonstrated superiority over the subjective evaluation of grayscale and color Doppler ultrasound findings by an experienced examiner.<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">5–7</span></a> Although these criteria increase the detection of malignant tumors, they also identify a high number of benign cysts and adnexal masses as “suspicious.” The importance of optimal management of adnexal masses lies in proper differentiation between benignity and malignancy to achieve better planning and execution of surgical treatment if necessary.</p><p id="par0070" class="elsevierStylePara elsevierViewall">In all cases, an assessment of the lesions was performed using the LR1, LR2, ADNEX, and simple rules models, comparing the result obtained with each model with the post-surgical histological result. The diagnostic accuracy of each model was initially evaluated using the IOTA-proposed cutoff. We also calculated its sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. When possible, the sensitivity and specificity of the different models were compared using McNemar's test.</p><p id="par0075" class="elsevierStylePara elsevierViewall">In addition, receiver operating characteristic (ROC) curves for the LR1, LR2, and ADNEX models were generated. The DeLong method was used to compare the area under the ROC curve between models.</p><p id="par0080" class="elsevierStylePara elsevierViewall">Moreover, we calculated new cutoff points using our population, for those models where a ROC curve was generated. For each model, two cutoff points were determined: (i) following Youden's criterion: the cutoff point being the closest to the upper left corner, “sensitivity” of the ROC curve and (ii) the cutoff point was determined by maximizing sensitivity.</p><p id="par0085" class="elsevierStylePara elsevierViewall">Finally, a precision–recall (PR) curve was generated for the LR1, LR2, and ADNEX models.</p><p id="par0090" class="elsevierStylePara elsevierViewall">The statistical significance level was established at <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05.</p><p id="par0095" class="elsevierStylePara elsevierViewall">This study was approved by the Biomedical Research Ethics Committee of the University Hospitals Virgen Macarena and Virgen del Rocío under protocol number MAC-2021/022-N-21.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0100" class="elsevierStylePara elsevierViewall">A total of 300 women who underwent surgery for a complex adnexal mass between 2017 and 2022 (64 months) were analyzed. The mean age of the patients included in the study was 54.63 years with a standard deviation of 14.10 years (range 16–91), 66.3% (199 women) were postmenopausal, while 33.7% (101 women) were of childbearing age.</p><p id="par0105" class="elsevierStylePara elsevierViewall">All patients were treated by expert operators. Only 4.3% (13 women) had a personal history of gynecological cancer, including breast cancer. Current use of hormonal therapy was found in a small percentage of the sample, 2.3% (7 women).</p><p id="par0110" class="elsevierStylePara elsevierViewall">The ultrasound characteristics of the lesions are summarized in the following table, as well as differentiating between benign, borderline and malignant categories (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0115" class="elsevierStylePara elsevierViewall">The patients underwent surgery within the 120 days following the ultrasound report. After surgery, 140 lesions were histologically classified as benign, 135 as malignant, and 25 as borderline. Therefore, the prevalence of malignancy in the study sample was 45%. Histological results showed serous carcinoma as the main diagnosis for malignancy, while epithelial histology was the primary diagnosis for benignity (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>A and B).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0120" class="elsevierStylePara elsevierViewall">LR1 and LR2 models, and ADNEX were applicable to all lesions; however, simple rules were inconclusive in 72 lesions (24%).</p><p id="par0125" class="elsevierStylePara elsevierViewall">The LR1 system, which uses a 10% cutoff for a high risk of malignancy, showed a sensitivity of 91% (95% CI: 85–95) and a specificity of 53% (95% CI: 45–61). Following Youden's criterion, in our sample, the optimal cutoff for the diagnosis of malignant tumors was 39.14%. Using this cutoff, the model's sensitivity was 89% (95% CI: 82–94) and specificity was 55% (95% CI: 48–63).</p><p id="par0130" class="elsevierStylePara elsevierViewall">The LR2 system, with a 10% cutoff, showed a sensitivity of 89% (95% CI: 82–94) and a specificity of 55% (95% CI: 48–63). The optimal cutoff following Youden's criterion increased to 56.66%, which decreased the sensitivity to 66.41% (95% CI: 57.75–74.34) and increased the specificity to 84.94% (95% CI: 78.58–90.01).</p><p id="par0135" class="elsevierStylePara elsevierViewall">The disparity in sensitivity and specificity results of the two models showed no significant differences, with <span class="elsevierStyleItalic">p</span>-values of 0.256 and 0.393, respectively. The analysis of the area under the ROC curve also showed no significant differences (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.095). However, the comparison of the PR (precision vs recall) curves showed statistically significant differences (95% CI: 0.008–0.037), indicating that the LR1 model (green) was superior to the LR2 model (red) (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>).</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0140" class="elsevierStylePara elsevierViewall">For the ADNEX model, we must consider that this tool not only estimates the risk of malignancy but also calculates in parallel the probability of the tumor being malignant in stage 1, malignant in stages 2–4, and finally, metastatic malignant. This feature makes the comparison among the other models difficult. For this reason, we included all malignant stages (stages 1, 2–4, and metastatic) in a single category called “ADNEX-malignancy”. For the ADNEX-malignancy model, a 10% cutoff was used for a high risk of malignancy. Using this cutoff, the model's sensitivity was 82% (95% CI: 75–88) and specificity was 61% (95% CI: 54–69). Comparing this model with LR1 and LR2 revealed statistically significant differences, with better sensitivity for LR1 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.004) and LR2 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.006), respectively. Meanwhile, specificity was higher for ADNEX compared to LR1 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.01) and LR2 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.02).</p><p id="par0145" class="elsevierStylePara elsevierViewall">Finally, the simple rules showed a sensitivity of 68% (95% CI: 59–76) and a specificity of 86% (95% CI: 79–91), demonstrating the best specificity results in our population.</p><p id="par0150" class="elsevierStylePara elsevierViewall">Then, we analyzed each model by creating subgroups based on menopausal status. The menopausal subgroup showed the tendency of performing better in all tested models. The ROC curve for the premenopausal group, showed significant differences for LR1 compared to LR2 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). However, there were no significant differences in the PR curve. For postmenopausal patients, we found significant differences for the ROC curves (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001) and PR curves (95% CI: 0.0066–0.0497).</p><p id="par0155" class="elsevierStylePara elsevierViewall">Analyzing the area under the ROC curve, which reflects the overall performance of the test, the best result was found for LR1 in either premenopausal (0.78) or postmenopausal (0.82) women.</p><p id="par0160" class="elsevierStylePara elsevierViewall">Following these lines, we present a comparison of the diagnostic performance and predictive capacity of the different models in the studied population, analyzing menopausal status for each model, highlighting the best results (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0165" class="elsevierStylePara elsevierViewall">Overall, we observed better performance for LR1 and simple rules, based on the following results:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">•</span><p id="par0170" class="elsevierStylePara elsevierViewall">Sensitivity: LR1 91%</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">•</span><p id="par0175" class="elsevierStylePara elsevierViewall">Specificity: simple rules 86%</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">•</span><p id="par0180" class="elsevierStylePara elsevierViewall">PPV: simple rules 79%</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">•</span><p id="par0185" class="elsevierStylePara elsevierViewall">NPV: LR1 88%</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">•</span><p id="par0190" class="elsevierStylePara elsevierViewall">LR+: simple rules 4.7</p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">•</span><p id="par0195" class="elsevierStylePara elsevierViewall">LR−: LR1 0.17</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">•</span><p id="par0200" class="elsevierStylePara elsevierViewall">DOR: simple rules 12.52</p></li><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">•</span><p id="par0205" class="elsevierStylePara elsevierViewall">ACC: simple rules 0.78</p></li></ul></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Discussion</span><p id="par0210" class="elsevierStylePara elsevierViewall">Since 2000, the (IOTA) group has collected data from over 29,000 patients from 60 centers worldwide with the aim of creating models that allow for effective preoperative triage of patients. External validation of the such models is crucial to assess their practical utility in different populations, which is why we consider this type of study to be useful.</p><p id="par0215" class="elsevierStylePara elsevierViewall">Ultrasound evaluation was carried out by experienced sonographers, as in most of the analyzed studies. Our results are similar to those reported in the literature. Logistic regression models offer a sensitivity and specificity of 92% and 87%, respectively, for LR1 and 92% and 86% for LR2.<a class="elsevierStyleCrossRefs" href="#bib0185"><span class="elsevierStyleSup">10,13</span></a> On the other hand, the performance of the ADNEX model for predicting malignancy offers a sensitivity of 96.5% and a specificity of 71.3% in validation data,<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">11</span></a> which were lower in our population.</p><p id="par0220" class="elsevierStylePara elsevierViewall">For the simple rules, we found similar results in our population to those found in the literature, with sensitivity ranging from 63 to 93% and specificity from 78 to 97%.<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">2,14,15</span></a> The proportion of non-classifiable masses in the literature is around 20–30%,<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">9,14,16,17</span></a> which is similar to the proportion found in our population.</p><p id="par0225" class="elsevierStylePara elsevierViewall">Using the IOTA group's initial results as a reference, we found lower results for our population, but still achieving a good performance in the diagnosis. For LR1 sensitivity 91% vs 92.7%<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">10</span></a> and specificity 53% vs 74.3%<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">10</span></a>; LR2 sensitivity 89% vs 89.9%<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">10</span></a> and specificity 55% vs 70.7%<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">10</span></a>; ADNEX sensitivity 82% vs 96.5%<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">11</span></a> and specificity 61% vs 71.3%<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">11</span></a> and simple rules sensitivity 68% vs 93%<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">9</span></a> and specificity 86% vs 90%.<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">9</span></a> These differences could be explained by the high prevalence of malignancy present in our sample compared to IOTA populations (27%<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">9</span></a>–25%<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">10</span></a>).</p><p id="par0230" class="elsevierStylePara elsevierViewall">Kaijser's meta-analysis, where various diagnostic tests are compared, evaluated 195 studies with a mean prevalence of malignancy of 27%, and found that simple rules and LR2 were the best indices based on the best results for sensitivity and specificity. However, not all the analyzed studies conducted subsequent histological verification.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">18</span></a> Van Holsbeke et al., 2012 found the best results for LR1 when based on the best AUC in a sample which malignancy prevalence was 26%,<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">13</span></a> which aligns with our results. Another study conducted in the US population, observed similarly better specificities and predictive positive value for simple rules compared to other models.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">19</span></a> Van Calster et al., 2015 showed a meta-analysis in which the ADNEX model appears to have similar, or even slightly superior, performance than LR2, as well as simple rules.<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">20</span></a> Their multicenter cohort study in 2020 concluded that the IOTA ADNEX model and the IOTA Simple Rules risk model are the models for characterizing ovarian lesions with the best performance, although histological verification was not obtained for all lesions (malignancy prevalence 20%).<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">21</span></a> In the Filipino population, LR1 and LR2 showed the best diagnostic yield, although lower sensitivity results were obtained, likely due to the different epidemiology of ovarian cancer in this population.<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">22</span></a> Likewise, according to the ESGO/ISUOG/IOTA/ESGE consensus, the IOTA ADNEX model or the simple rules risk model could be applied as the first step to determine the risk of malignancy in suspicious lesions, ideally by experienced operators.<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">23</span></a> The differences found in our results may have arised as a consequence of grouping all malignancy subgroups in the analysis, which in turn, could condition lower sensitivity and specificity results.</p><p id="par0235" class="elsevierStylePara elsevierViewall">Testa et al., 2014 points out the importance of incorporating LR2 or simple rules in adnexal mass evaluation.<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">24</span></a> Conversely, our results highlights the opposite during the first step in classifying an adnexal mass, which is to follow simple rules or the LR1 model.</p><p id="par0240" class="elsevierStylePara elsevierViewall">Regarding hormonal status, the 2022 Cochrane review found the best sensitivity for menopausal women for ADNEX (97.6%), followed by LR2 (94.8%).<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">25</span></a> These results differ from those found in our population, where we found the best performance for LR1 (93%) and LR2 (89%). Additionally, we found that overall, there was a better diagnostic performance in our population for menopausal women, aligning with the systematic review of Davenport et al., 2022, which found lower specificities for tests in the premenopausal state.<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">25</span></a></p><p id="par0245" class="elsevierStylePara elsevierViewall">However, according to Sayanesh et al., 2013, LR2 presents higher yield in premenopausal women,<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">26</span></a> similar to Kaijser et al., 2014 who also included simple rules.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">18</span></a> Regarding menopausal status, the results in the literature are mixed, so more studies are needed to evaluate tests based on hormonal status, as the highest prevalence of ovarian cancer occurs during menopause. The differences among different studies may be due to cohort selection, and the prevalence of ovarian cancer in the samples of different analyzed studies. The prevalence of ovarian cancer was 29% and 27%,<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">18,26</span></a> compared to our population where the prevalence of malignancy was 44%, in an older population, in which 66.3% of women were in menopause.</p><p id="par0250" class="elsevierStylePara elsevierViewall">The American College of Radiology proposed The Ovarian-Adnexal Reporting and Data System (O-RADS) for ultrasound, as a risk stratification and management system to provide consistent interpretations. This system incorporates ultrasound descriptors that can be used to categorize the vast majority of adnexal lesions and their clinical management.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">27</span></a> The current trend is to use this system. Accordingly, all the lesions evaluated in our study would be categorized as ORADS 4 or 5, requiring management by a gyn-oncologist; therefore, it would not have provided any advantage over IOTA systems.</p><p id="par0255" class="elsevierStylePara elsevierViewall">The fact that the models were only applied in high-risk populations who were subjected to surgery intervention may imply a selection bias in our study. Therefore, data extrapolation is difficult for low-risk populations, i.e., women who do not require surgery. Thus, the incidence of ovarian cancer in our sample might be much higher than observed in the society. The accuracy of these tests may vary for women undergoing tests in non-specialized healthcare settings. Similarly, the prevalence of benign lesions may have been underestimated, as surgery is generally required for more complex lesions. However, these are the ones that require our attention due to the implications for survival and prognosis of the disease resulting from early diagnosis. The fact that the data were collected at a single center by experienced examiners presents the advantage that classification criteria are more homogeneous.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Conclusion</span><p id="par0260" class="elsevierStylePara elsevierViewall">Our study suggests that, in the subgroup of patients with adnexal masses that required surgery, the IOTA risk stratification through LR1 shows higher sensitivity for risk stratification of malignancy, while simple rules has the highest specificity and diagnostic accuracy; however, it is inconclusive in almost one out of every four adnexal masses. Additionally, LR1–LR2 and ADNEX do not show significant differences in diagnostic accuracy.</p><p id="par0265" class="elsevierStylePara elsevierViewall">The application of the IOTA risk stratification allows for proper preoperative management of adnexal masses. The IOTA risk stratification through LR1 and simple rules could serve as a model of good precision and ease of application in daily clinical practice. Prospective studies that could corroborate our results would be desirable, as well as extending them to subgroup of patients with a lower incidence of malignancy.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Ethical disclosures</span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Protection of human and animals</span><p id="par0270" class="elsevierStylePara elsevierViewall">The authors declare that no experiments involving humans or animals subjects were conducted in the course of this research.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Data confidentiality</span><p id="par0275" class="elsevierStylePara elsevierViewall">The authors declare that they have followed the protocols of the workplace regarding the publication of patient data.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Right to privacy and informed consent</span><p id="par0280" class="elsevierStylePara elsevierViewall">The authors declare that no patient data appear in this article.</p></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Ethics of approval statement</span><p id="par0285" class="elsevierStylePara elsevierViewall">This study was approved by the Biomedical Research Ethics Committee of the University Hospitals Virgen Macarena and Virgen del Rocío under protocol number MAC-2021/022-N-21.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Funding</span><p id="par0290" class="elsevierStylePara elsevierViewall">The authors declare no received funding.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Patient consent</span><p id="par0295" class="elsevierStylePara elsevierViewall">This study obtained the informed consent of the participants and was approved by the Biomedical Research Ethics Committee of the University Hospitals Virgen Macarena and Virgen del Rocío under protocol number MAC-2021/022-N-21.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Conflict of interest</span><p id="par0300" class="elsevierStylePara elsevierViewall">The authors declare no potential conflict of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:15 [ 0 => array:3 [ "identificador" => "xres2283091" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Purpose" ] 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" => "xpalclavsec1899366" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres2283090" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1899367" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Materials methods" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0025" "titulo" => "Conclusion" ] 9 => array:3 [ "identificador" => "sec0030" "titulo" => "Ethical disclosures" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0035" "titulo" => "Protection of human and animals" ] 1 => array:2 [ "identificador" => "sec0040" "titulo" => "Data confidentiality" ] 2 => array:2 [ "identificador" => "sec0045" "titulo" => "Right to privacy and informed consent" ] ] ] 10 => array:2 [ "identificador" => "sec0050" "titulo" => "Ethics of approval statement" ] 11 => array:2 [ "identificador" => "sec0055" "titulo" => "Funding" ] 12 => array:2 [ "identificador" => "sec0060" "titulo" => "Patient consent" ] 13 => array:2 [ "identificador" => "sec0065" "titulo" => "Conflict of interest" ] 14 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2024-06-15" "fechaAceptado" => "2024-09-15" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1899366" "palabras" => array:4 [ 0 => "Ovarian cancer" 1 => "Adnexal mass" 2 => "Ultrasound" 3 => "IOTA model" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1899367" "palabras" => array:4 [ 0 => "Cáncer de ovario" 1 => "Masa anexial" 2 => "Ecografía" 3 => "Modelos IOTA" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Purpose</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To evaluate the diagnostic accuracy of the <span class="elsevierStyleItalic">International Ovarian Tumor Analysis (IOTA)</span> Logistic Regression Model 1, 2 (LR1, LR2) ADNEX model and IOTA Simple Rules, in the pre-surgical evaluation of ovarian tumors classified as complex adnexal masses.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">This is a cross-sectional observational study of diagnostic accuracy. We will select patients, who undergo surgical intervention due to adnexal mass with indeterminate, intermediate or high suspicion of malignancy (GI-RADS 4–5), as assessed by an expert ultrasound operator. We analyzed and compared the diagnostic performance and predictive capacity of the different models in the studied population, and also we analyzed each model by creating subgroups based on menopausal status.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">One hundred thirty five malignant masses (45%), one hundred forty benign (46.7%) and twenty five border line (8.3%) were included.</p><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">LR1 and LR2 models, and ADNEX were applicable to all lesions; however, in 72 lesions (24%), the simple rules were inconclusive.</p><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">We observed better performance for LR1 and simple rules, based on the following results: Sensitivity: LR1 91%. Specificity: simple rules 86%. PPV: simple rules 79%. NPV: LR1 88%.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Our study suggests that the subgroup of patients with complex adnexal masses, the IOTA risk stratification through LR1 shows higher sensitivity for risk stratification of malignancy, while simple rules has the highest specificity and diagnostic accuracy. However, it is inconclusive in one out of every four adnexal masses. Additionally, LR1–LR2 and ADNEX do not show significant differences in diagnostic accuracy.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Purpose" ] 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="spar0035" class="elsevierStyleSimplePara elsevierViewall">Evaluar la precisión diagnóstica de los modelos de valoración ecográfica de masas anexiales del grupo de Análisis Internacional de Tumores de Ovario (IOTA), Modelo de regresión logística 1, 2 (LR1, LR2), modelo ADNEX y Reglas simples, en la evaluación prequirúrgica de tumoraciones de ovario clasificados como masas anexiales complejas.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Métodos</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Se trata de un estudio observacional transversal de precisión diagnóstica. Seleccionaremos a pacientes diagnosticadas de masa anexial compleja, a quienes se les realizará una evaluación ecográfica por parte de un ecografista experto, con una clasificación como GIRADS 4-5, y de las cuales tengamos un diagnóstico histológico. Analizamos y comparamos el rendimiento diagnóstico y la capacidad predictiva de los diferentes modelos en la población estudiada, y también analizamos cada modelo creando subgrupos en función del estado menopáusico.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Se incluyeron 135 masas malignas (45%), 140 benignas (46,7%) y 25 borderline (8,3%). Los modelos LR1, LR2 y ADNEX fueron aplicables a todas las lesiones; sin embargo, en 72 lesiones (24%), las reglas simples no fueron concluyentes.</p><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Observamos un mejor rendimiento para LR1 y las reglas simples, según los siguientes resultados: sensibilidad: LR1 91%; especificidad: reglas simples 86%; PPV: reglas simples 79%, y VPN: LR1 88%.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Nuestro estudio sugiere que, en el subgrupo de pacientes con masas anexiales complejas, la estratificación del riesgo de IOTA mediante LR1 muestra una mejor sensibilidad para el riesgo de malignidad, mientras que las reglas simples presentan la mejor especificidad y exactitud diagnóstica; sin embargo, no es concluyente en casi una de cada 4 tumoraciones. Los sistemas LR1-LR2 y ADNEX no mostraron diferencias significativas en cuanto a exactitud diagnóstica.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "multimedia" => array:4 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 757 "Ancho" => 1675 "Tamanyo" => 84588 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">(A) Malignant histology results. (B) Benign histology results.</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" => 956 "Ancho" => 1675 "Tamanyo" => 85911 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">PR curve for LR1–LR2 superimposed, the green color represents LR1 model and the red one represents LR2 model. The curve of LR1 model is higher than LR2 model.</p>" ] ] 2 => 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: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="" 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">General \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">Benign \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">Borderline \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">Malignant \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="elsevierStyleBold">Maximum diameter (median)</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">90<span class="elsevierStyleHsp" style=""></span>mm (±75<span class="elsevierStyleHsp" style=""></span>mm) \t\t\t\t\t\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<span class="elsevierStyleHsp" style=""></span>mm \t\t\t\t\t\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">80<span class="elsevierStyleHsp" style=""></span>mm \t\t\t\t\t\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">100<span class="elsevierStyleHsp" style=""></span>mm \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="elsevierStyleBold">Echogenicity</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><span class="elsevierStyleItalic">Solid</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">26.7% (80) \t\t\t\t\t\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">22.1% (31) \t\t\t\t\t\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">4% (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">35.5% (48) \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><span class="elsevierStyleItalic">Multilocular</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">58.7% (176) \t\t\t\t\t\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">61.4% (86) \t\t\t\t\t\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">7.2% (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">53.3% (72) \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><span class="elsevierStyleHsp" style=""></span>- >10 loculi \t\t\t\t\t\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% (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">10.7% (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">1.2% (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">8.8% (12) \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="elsevierStyleBold">Solid area</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><span class="elsevierStyleItalic">Presence</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">66.6% (198) \t\t\t\t\t\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">44.3% (62) \t\t\t\t\t\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">80% (20) \t\t\t\t\t\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">85.9% (116) \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><span class="elsevierStyleItalic">Size (median)</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">45<span class="elsevierStyleHsp" style=""></span>mm (±45<span class="elsevierStyleHsp" style=""></span>mm) \t\t\t\t\t\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<span class="elsevierStyleHsp" style=""></span>mm \t\t\t\t\t\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">19<span class="elsevierStyleHsp" style=""></span>mm \t\t\t\t\t\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">45<span class="elsevierStyleHsp" style=""></span>mm \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><span class="elsevierStyleItalic">Acoustic shadow</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">4% (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">7.9% (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">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">0.7% (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><span class="elsevierStyleItalic">Internal irregular wall</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">34% (102) \t\t\t\t\t\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">22.1% (31) \t\t\t\t\t\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">36% (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">45.9% (62) \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">Papillae</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><span class="elsevierStyleItalic">Presence</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">31.3% (94) \t\t\t\t\t\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">18.6% (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">72% (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">37% (50) \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><span class="elsevierStyleItalic">Vascularized</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">57.4% (54) \t\t\t\t\t\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">30% (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">44.4% (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">76% (38) \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><span class="elsevierStyleItalic">Single</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">37.2% (35) \t\t\t\t\t\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">65.38% (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">27.7% (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">28% (14) \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><span class="elsevierStyleItalic">Double</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">10.6% (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">15.4% (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">11.1% (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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8% (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><span class="elsevierStyleItalic">Triple</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">8% (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">7.6% (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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.1% (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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8% (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><span class="elsevierStyleItalic">Quadruple</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">42.5% (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">11.5% (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">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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56% (28) \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="elsevierStyleBold">Color score</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><span class="elsevierStyleItalic">I</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">33.7% (101) \t\t\t\t\t\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">53.6% (75) \t\t\t\t\t\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">4% (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">11.9% (16) \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><span class="elsevierStyleItalic">II</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">23.7% (71) \t\t\t\t\t\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">30% (42) \t\t\t\t\t\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">24% (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">17% (23) \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><span class="elsevierStyleItalic">III</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">25.0% (75) \t\t\t\t\t\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">13.6% (19) \t\t\t\t\t\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">12% (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">39.3% (53) \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><span class="elsevierStyleItalic">IV</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">17.7% (53) \t\t\t\t\t\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">2.8% (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">24% (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">31.9% (43) \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><span class="elsevierStyleItalic">Ascites</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">11.7% (35) \t\t\t\t\t\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">3.6% (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">8% (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">20.7% (28) \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><span class="elsevierStyleItalic">Pain during exploration</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">11.3% (34) \t\t\t\t\t\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">9.3% (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">8% (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">14.1% (19) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3702344.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Ultrasound characteristics of the analyzed lesions.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">S<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>sensitivity, Sp<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>specificity, PPV<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>positive predictive value, NPV<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>negative predictive value, LR+<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>positive likelihood ratio, LR−<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>negative likelihood ratio, DOR<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>diagnostic odds ratio, Acc=<span class="elsevierStyleHsp" style=""></span>accuracy, AUC ROC<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>area under the curve diagnostic odds ratio.</p><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Green shading is used to indicate the best results.</p>" "tablatextoimagen" => array:4 [ 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 " colspan="11" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LR1</th></tr><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">S \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">Sp \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">PPV \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">NPV \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">LR+ \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">LR− \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">DOR \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">Acc \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">AUC ROC \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">Youden \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">General \t\t\t\t\t\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">0.91 (0.85–0.95) \t\t\t\t\t\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">0.53 (0.45–0.61) \t\t\t\t\t\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">0.61 (0.54–0.68) \t\t\t\t\t\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">0.88 (0.80–0.94) \t\t\t\t\t\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">1.94 (1.63–2.30) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.17 (0.10–0.30) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.47 (5.88–22.32) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.7 (0.64–0.75) \t\t\t\t\t\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">0.91 (0.85–0.95) \t\t\t\t\t\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">0.53 (0.65–0.79) \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">Pre-menopausal \t\t\t\t\t\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">0.82 (0.63–0.94) \t\t\t\t\t\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">0.56 (0.44–0.68) \t\t\t\t\t\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">0.42 (0.29–0.56) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.89 (0.76–0.96) \t\t\t\t\t\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">1.87 (1.37–2.56) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.32 (0.14–0.72) \t\t\t\t\t\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">5.89 (2.02–17.21) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.63 (0.53–0.73) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.78 (0.68–0.88) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">36.05% \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">Post-menopausal \t\t\t\t\t\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">0.93 (0.87–0.97) \t\t\t\t\t\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">0.51 (0.40–0.61) \t\t\t\t\t\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">0.68 (0.60–0.76) \t\t\t\t\t\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">0.13 (0.06–0.76) \t\t\t\t\t\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">1.89 (1.53–2.33) \t\t\t\t\t\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">0.13 (0.06–0.27) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14.45 (6.07–34.41) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.73 (0.66–0.79) \t\t\t\t\t\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">0.82 (0.76–0.88) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39.30% \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3702343.png" ] ] 1 => 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 " colspan="11" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LR2</th></tr><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">S \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">Sp \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">PPV \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">NPV \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">LR+ \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">LR− \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">DOR \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">Acc \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">AUC ROC \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">Youden \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">General \t\t\t\t\t\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">0.89 (0.82–0.94) \t\t\t\t\t\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">0.55 (0.48–0.63) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.62 (0.54–0.69) \t\t\t\t\t\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">0.86 (0.78–0.92) \t\t\t\t\t\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">1.99 (1.66–2.38) \t\t\t\t\t\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">0.20 (0.12–0.33) \t\t\t\t\t\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">9.86 (5.32–12.30) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.7 (0.65–0.75) \t\t\t\t\t\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">0.89 (0.82–0.94) \t\t\t\t\t\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">0.55 (0.48–0.63) \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">Pre-menopausal \t\t\t\t\t\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">0.89 (0.72–0.98) \t\t\t\t\t\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">0.55 (0.43.–0.66) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.43 (0.30–0.57) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.93 (0.81–0.99) \t\t\t\t\t\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">1.98 (1.49–2.62) \t\t\t\t\t\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">0.20 (0.07–0.58) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.10 (2.80–35.44) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.64 (0.54–0.74) \t\t\t\t\t\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">0.77 (0.67–0.88) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.64% \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">Post-menopausal \t\t\t\t\t\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">0.89 (0.72–0.98) \t\t\t\t\t\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">0.55 (0.43–0.66) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.43 (0.30–0.57) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.93 (0.81–0.99) \t\t\t\t\t\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">1.98 (1.49–2.62) \t\t\t\t\t\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">0.20 (0.07–0.58) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.10 (2.80–36.4) \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">0.64 (0.54–0.74) \t\t\t\t\t\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">0.81 (0.75–0.87) \t\t\t\t\t\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">43.31% \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3702342.png" ] ] 2 => 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 " colspan="10" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">ADNEX-malignancy</th></tr><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">S \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">Sp \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">PPV \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">NPV \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">LR+ \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">LR− \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">DOR \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">Acc \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">AUC ROC \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">General \t\t\t\t\t\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">0.82 (0.75–0.88) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61 (0.54–0.69) \t\t\t\t\t\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">0.63 (0.56–0.70) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.81 (0.73–0.87) \t\t\t\t\t\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">2.13 (1.73–2.62) \t\t\t\t\t\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">0.29 (0.20–0.43) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.30 (4.25–12.54) \t\t\t\t\t\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">0.71 (0.65–0.76) \t\t\t\t\t\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">0.84 (0.80–0.89) \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">Pre-menopausal \t\t\t\t\t\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">0.79 (0.59–0.92) \t\t\t\t\t\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">0.59 (0.47–0.70) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.42 (0.29–0.57) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.88 (0.75–0.95) \t\t\t\t\t\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">1.91 (1.37–2.68) \t\t\t\t\t\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">0.36 (0.17–0.76) \t\t\t\t\t\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">5.25 (1.90–14.52) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.64 (0.54–0.73) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.67 (0.56–0.79) \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">Post-menopausal \t\t\t\t\t\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">0.83 (0.74–0.90) \t\t\t\t\t\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">0.61 (0.50–0.72) \t\t\t\t\t\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">0.72 (0.63–0.80) \t\t\t\t\t\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">0.75 (0.63–0.84) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.15 (1.63–2.83) \t\t\t\t\t\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">0.28 (0.18–0.44 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.76 (3.99–15.09) \t\t\t\t\t\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">0.73 (0.66–0.79) \t\t\t\t\t\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">0.56 (0.48–0.64) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3702345.png" ] ] 3 => 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 " colspan="9" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Simple rules</th></tr><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">S \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">Sp \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">PPV \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">NPV \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">LR+ \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">LR− \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">DOR \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">Acc \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">General \t\t\t\t\t\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">0.68 (0.59–0.76) \t\t\t\t\t\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">0.86 (0.79–0.91) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.79 (0.71–0.86) \t\t\t\t\t\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">0.77 (0.70–0.83) \t\t\t\t\t\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">4.7 (3.19–6.92) \t\t\t\t\t\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">0.38 (0.29–0.48) \t\t\t\t\t\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">12.52 (7.12–22.02) \t\t\t\t\t\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">0.78 (0.72–0.83) \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">Pre-menopausal \t\t\t\t\t\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">0.57 (0.37–0.76) \t\t\t\t\t\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">0.85 (0.75–0.92) \t\t\t\t\t\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">0.59 (0.39–0.78) \t\t\t\t\t\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">0.84 (0.73–0.91) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.79 (2.02–7.13) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.50 (0.33–0.78) \t\t\t\t\t\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">7.51 (2.80–20.13) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">77.23% (67.81–84.98) \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">Post-menopausal \t\t\t\t\t\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">0.71 (0.61–0.79) \t\t\t\t\t\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">0.86 (0.77–0.92) \t\t\t\t\t\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">0.85 (0.76–0.92) \t\t\t\t\t\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">0.72 (0.63–0.80) \t\t\t\t\t\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">5.06 (3.01–8.50) \t\t\t\t\t\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">0.34 (0.25–0.46) \t\t\t\t\t\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">14.88 (7.24–30.59) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">77.89% (71.48–83.45) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3702341.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Diagnostic performance determinants for LR1, LR2, ADNEX model and simple rules.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:27 [ 0 => array:3 [ "identificador" => "bib0140" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:1 [ "referenciaCompleta" => "Muto M. 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