Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer | Revista Española de Medicina Nuclear e Imagen Molecular (English Edition)

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A: imágenes transaxiales de PET/TC; B: distribución de niveles de grises; C: imagen de un tumor del subtipo luminal A extraído en 3D. SUVmáx: 4,25, histograma-uniformidad: 0,30, GLCM-contraste: 0,87, GLCM-entropía: 2,95, GLCM-energía: 0,05." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "H. Önner, N. Coskun, M. Erol, M.İ.E. Karanis" "autores" => array:4 [ 0 => array:2 [ "nombre" => "H." "apellidos" => "Önner" ] 1 => array:2 [ "nombre" => "N." "apellidos" => "Coskun" ] 2 => array:2 [ "nombre" => "M." "apellidos" => "Erol" ] 3 => array:2 [ "nombre" => "M.İ.E." 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WBCS (knees, anterior views) at 30min, 4h and 8 or 24h (left column: one-day-protocol, right column: two-days-protocol). Note the progressive increasing uptake (arrows) in intensity in one-day-protocol. In contrast, in two-days-protocol a decreasing uptake between the 4h and 8h images was observed. Biopsy: reported acute inflammation; therefore, false positive in one-day-protocol and true negative in two-days-protocol." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Edel Noriega-Álvarez, Ana M. García Vicente, Francisco J. Pena Pardo, Germán A. Jiménez Londoño, Mariano Amo-Salas, Ana M. Benítez Segura, María T. Bajén Lázaro, Jaime Mora Salvadó, Cristina Gámez Cenzano, Ángel M. Soriano Castrejón" "autores" => array:10 [ 0 => array:2 [ "nombre" => "Edel" "apellidos" => "Noriega-Álvarez" ] 1 => array:2 [ "nombre" => "Ana M." "apellidos" => "García Vicente" ] 2 => array:2 [ "nombre" => "Francisco J." "apellidos" => "Pena Pardo" ] 3 => array:2 [ "nombre" => "Germán A." 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characteristics in invasive ductal breast cancer" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "11" "paginaFinal" => "16" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Hasan Önner, Nazim Coskun, Mustafa Erol, Meryem İlkay Eren Karanis" "autores" => array:4 [ 0 => array:4 [ "nombre" => "Hasan" "apellidos" => "Önner" "email" => array:1 [ 0 => "hasanonner_1988@hotmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Nazim" "apellidos" => "Coskun" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "Mustafa" "apellidos" => "Erol" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "Meryem İlkay" "apellidos" => "Eren Karanis" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Konya City Hospital, Konya, Turkey" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Ankara City Hospital, Ankara, Turkey" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Asociación de características texturales de PET/TC 18F-FDG con características inmunohistoquímicas en el cáncer de mama ductal invasivo" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 827 "Ancho" => 2500 "Tamanyo" => 97521 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Example of some textural features and SUVmax in a patient with triple-negative subtype. A shows transaxial PET/CT images, B represents grey-level distribution and C displays the image of a 3D extracted triple-negative subtype tumor, SUVmax: 16.41, histogram-uniformity: 0.14, GLCM-contrast: 2.14, GLCM-entropy: 4.80, GLCM-energy: 0.17." ] ] ] "textoCompleto" => "IntroductionCancers are heterogeneous cell mixtures that differ in their morphology, genetics, and biological behavior.<a class="elsevierStyleCrossRef" href="#bib0005">1</a> Heterogeneity is one of the main features of cancer biology and is associated with worse prognosis due to aggressive biological behavior and treatment failures.<a class="elsevierStyleCrossRef" href="#bib0010">2</a> Breast cancer (BC) is a highly heterogeneous type of cancer, and various factors such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2) status are responsible for tumor heterogeneity.<a class="elsevierStyleCrossRef" href="#bib0010">2</a> Apart from these, many factors such as tumor hypoxia, angiogenesis, necrosis, fibrosis, and cell proliferation play a role in tumor heterogeneity.<a class="elsevierStyleCrossRef" href="#bib0015">3</a> However, tumor heterogeneity cannot be determined precisely as biopsy samples obtained only from an area of the tumor cannot reflect the exact size of the phenotypic and genetic variations.<a class="elsevierStyleCrossRefs" href="#bib0010">2,4</a> Therefore, an accurate diagnosis is attempted by taking biopsy samples from more than one region of the tumor. However, more aggressive areas may still be ignored due to heterogeneity.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> It is an important requirement to demonstrate the heterogeneity of the entire tumor noninvasively, to understand its nature, to predict prognostic factors, and to plan treatment management.<a class="elsevierStyleCrossRef" href="#bib0010">2</a>Fluoride-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is widely used in BC for staging, recurrence detection, and assessing response to treatment. Standardized uptake value (SUV) is a semi-quantitative parameter that providing prognostic information by showing the degree of 18F-FDG uptake.<a class="elsevierStyleCrossRef" href="#bib0030">6</a> Among the SUV parameters, the most commonly used maximum standardized uptake value (SUVmax) expresses the single voxel value representing the most intense 18F-FDG uptake in the tumor, and cannot accurately reflect glucose metabolism especially in heterogeneous tumors such as BC. It is also influenced by various factors such as body weight, plasma glucose level, partial volume effects, and time of injection.<a class="elsevierStyleCrossRefs" href="#bib0035">7,8</a> For these reasons, novel parameters are needed to better understand tumor heterogeneity and to guide clinicians for personalized treatments. Texture analysis consists of several mathematical methods that describe the relationship between the grey-level intensity of voxels and their positions within a defined volume of interest.<a class="elsevierStyleCrossRef" href="#bib0045">9</a> Briefly, it is used to measure tumor heterogeneity by enabling the tumors to be visualized as granulate or rough. In recent years, interest in tumor texture analysis by 18F-FDG PET/CT scan has been increased in the field of oncology. In transgenic mice with orthotopically implanted breast tumors, heterogeneity calculated in-vivo from PET images accurately reflects the heterogeneity of ex-vivo tracer uptake calculated directly from autoradiographic images.<a class="elsevierStyleCrossRef" href="#bib0050">10</a> Also, several studies on different types of tumors have found that texture analysis performs better in distinguishing tumor histology, predicting treatment response, and assessing correlation with prognostic factors.<a class="elsevierStyleCrossRefs" href="#bib0055">11–15</a>In this context; this study aims to investigate the relationship between primary tumor textural features (TFs) extracted from 18F-FDG PET/CT and IHCs, and to evaluate the complementary role of TFs for evaluating the aggressiveness of invasive ductal breast cancer (IDBC).Materials and methodsInclusion and exclusion criteriaThis retrospective study was approved by the Institutional Review Board (Approval No: 41901325-050.99). Written informed consent was obtained from all patients who underwent PET/CT for clinical indications, accepting that their data could be used in clinical trials. Patients with IDBC who underwent 18F-FDG PET/CT exams before any surgery and/or any therapeutic interventions between June 2017 and December 2019 were included. Exclusion criteria were as follows: those with tumors smaller than 1.5cm (to minimize the partial volume effect), those with tumors less than 64 pixels of grey-level (because advanced texture analysis is not obtainable), and those with local advanced or advanced-stage tumors (requiring pre-operative treatment before surgery). Patients who did not bear these exclusion criteria were included consecutively.18F-FDG PET/CT imaging protocolSince the blood glucose level should be less than 200mg/dL, patients fasted for at least four hours before the examination. A Siemens Biograph LSO-16 PET/CT scanner (Siemens Medical Solutions, Chicago, IL) was used. 18F-FDG was intravenously administered at a dose of 236.8–510.6MBq (6.4–13.8mCi) according to body weight (3.7MBq/kg). Computed tomography (CT) was performed on a 4-slice spiral CT using a slice thickness of 5mm (120–150kV, 80mA). After the transmission scan, 3D PET acquisition was obtained by six to eight two minute sessions in the bed position. For attenuation correction of PET data, CT images were used.Image analysis and tumor segmentationThe images were visually assessed by two experienced nuclear medicine specialists in a consensus. Primary breast tumors on PET/CT images were delineated and SUVmax and TFs were calculated through PET images using the LIFEx software package program (Orsay, France) (10). Second and higher-order imaging parameters were extracted only for lesions greater than 64 voxels. The first-order TFs which were obtained for this study included histogram and shape-based features. In the further textural analysis, grey-level co-occurrence matrix (GLCM), grey-level run-length matrix (GLRLM), neighborhood grey-level different matrix (NGLDM), and grey-level zone length matrix (GLZLM) parameters were obtained. As a result, SUVmax and 37TFs of primary breast tumor were extracted.Histopathologic evaluationAll included patients underwent clinically selected appropriate surgical treatment (partial mastectomy or transverse rectus abdominal flap reconstruction and skin-sparing mastectomy, sentinel lymph node biopsy, or axillary lymph node dissection) in a median duration of 23 (13–46) days after 18F-FDG PET/CT imaging. All IHCs were evaluated from surgical samples. Both proportional staining (%) and densities (1+, 2+, and 3+) were taken into account to evaluate the ER and PR status of tumor cells. HER-2 gene expression status was considered positive when membrane immune staining was 3+, negative when it was 1+ or no staining was seen. The fluorescent in situ hybridization (FISH) method was used to confirm the presence of HER-2 gene expression when scores were 2+. The Scarff Bloom Richardson classification system was used to evaluate histopathological grade.<a class="elsevierStyleCrossRefs" href="#bib0075">15,16</a> The patients with grade I or II tumors were classified as having low-grade and those with grade III tumors were classified as having high-grade tumors. The Ki-67 proliferation index was evaluated according to hot spot areas. In addition, lymph node metastasis status and tumor diameter were obtained from post-operative pathology reports. The clinical prognostic staging was accompanied by histopathological findings, as reported in the American Cancer Joint Committee Cancer Staging Manual, 8th edition.<a class="elsevierStyleCrossRef" href="#bib0085">17</a> The tumors were classified into the molecular subtypes<a class="elsevierStyleCrossRef" href="#bib0090">18</a> as follows:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005">1Luminal A (Lum A): ER-positive and/or PR-positive, HER-2-negative, and a Ki-67 proliferation index less than 14%.</li><li class="elsevierStyleListItem" id="lsti0010">2Luminal B HER-2 negative (Lum B-): ER-positive and/or PR-positive, HER-2-negative, and a Ki-67 proliferation index at least 14%.</li><li class="elsevierStyleListItem" id="lsti0015">3Luminal B HER-2 positive (Lum B+): ER-positive and/or PR-positive, HER-2-positive, and any Ki-67 proliferation index.</li><li class="elsevierStyleListItem" id="lsti0020">4HER-2 positive: ER-negative, PR-negative, HER-2-positive.</li><li class="elsevierStyleListItem" id="lsti0025">5Triple-negative (TN): ER-negative, PR-negative, HER-2-negative.</li></ul>Statistical analysisFor statistical analyses, IBM SPSS Statistics for Windows, Version 21.0 (IBM Corp., Armonk, NY) was used. Nonparametric tests were used in all comparisons because of the non-homogeneity of the data. Spearman's rank correlation test was used to evaluate the correlation between TFs and SUVmax. The diagnostic performance of quantitative parameters for predicting prognostic features were evaluated with the receiver operating characteristic (ROC) curves. For multivariate analysis, the area under the curve (AUC) for each TF was evaluated and those with the highest AUC in their respective category were included in multivariate logistic regression models. Thus, variables independently related to ER-negativity, PR-negativity, HER-2-positivity, lymph node metastasis, high-grade, increased Ki-67 proliferation index, and triple-negativity were sought. Power of estimation was Hosmer-Lemeshow goodness of fit statistics was used for the evaluation of model fit.ResultsThe study population consisted of 124 patients (median age 53, range 29–85 years). Fourteen patients (11.3%) had grade I, 65 (52.4%) had grade II, and 45 (36.3%) had grade III tumors. There were 34 (30.6%) tumors with a Ki-67 proliferation index of less than 14%. The positivity rates of ER, PR, and HER-2 were 100 (80.6%), 86 (69.4%), and 37 (29.8%), respectively. The frequency of molecular subtypes were as follows; 31 (25%) patients had Lum A, 42 (33.9%) had Lum B-, 28 (22.6%) had Lum B+, 9 (7.2%) had HER-2+, and 14 (11.3%) had TN subtype. Eighty-four (67.8%) patients had axillary lymph node metastasis. Seven (5.6%) patients had stage IA, 28 (22.6%) had stage IB, 40 (32.3%) had stage IIA, 17 (13.7%) had stage IIB, 18 (14.5%) had stage IIIA, and 14 (11.3%) had stage IIIB disease.The patients’ characteristics are summarized in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. <a class="elsevierStyleCrossRefs" href="#fig0005">Figs. 1 and 2</a> illustrate PET/CT and radiomics images of two different IDBC subtypes.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia>The Median SUVmax value of ER-negative tumors (17.05) was significantly higher than ER-positive ones (7.93) (p<0.001). Significant differences were found in 30 of 37TFs in terms of ER status. Moreover, GLCM-contrast was independently associated with ER-negativity in logistic regression analysis (OR=0.726 95% CI 0.561–0.939, p=0.015, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). The PR-negative tumors had significantly higher SUVmax values compared with PR-positive ones (median: 12.84 vs 7.43, p=0.001). Twenty-eight of 37TFs showed a significant difference between PR-negative and PR-positive patients. Logistic regression analysis revealed that GLZLM-GLNU was independently associated with PR-negativity with an OR of 0.9 (95% CI 0.85 – 0.95, p<0.001, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). The tumors with HER-2 gene expression had higher metabolic activity than those without HER-2 gene expression (median SUVmax: 11.62 vs. 7.72, p=0.009). Also, 21 out of 37TFs were significantly different between HER-2-positive and HER-2-negative tumors. Histogram-uniformity was independently associated with HER-2-positivity with an OR of 0.028 (95% CI 0.001 – 0.683, p=0.02, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>A significant difference was observed in SUVmax values across different grade groups (p<0.001). When TFs were compared between the low-grade (grade I and II) and high-grade (grade III) groups, a significant difference was observed in 31 of 37TFs. However, the only variable that showed an independent association with a high-grade was SUVmax (OR: 1.126, 95% CI 1.06–1.18, p<0.001, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). Ki-67 proliferation index was moderately correlated with SUVmax (r: 0.464, p<0.001). The median SUVmax value was also significantly higher in tumors with an increased Ki-67 proliferation index (11.58 for Ki-67≥14% vs. 5.29 for Ki-67<14%, p<0.001). The logistic regression model of the parameters with the highest AUC value revealed that shape sphericity was an independent predictor of high proliferation (Log OR: 13.80, 95% CI 1.57–26.04, p=0.027, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).Molecular subtypes were associated with significantly different SUVmax values (p<0.001). The median SUVmax was the lowest in Lum A subtype (5.22, range: 1.48–21.62) and the highest in TN (17.05 range: 7.72–42.88). Thirty-three out of 37TFs showed a significant difference across molecular subtypes. However, only SUVmax was independently associated with triple-negativity (OR: 1.13, 95% CI 1.01–1.26, p<0.001, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).The TFs that showed a strong positive correlation (r>0.95) with SUVmax were GLRLM-SRHGE, GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity (0.984, 0.984, 0.979, 0.973, 0.969, 0.968, and 0.965, respectively) while those which had a strong negative correlation (r<-0.95) with SUVmax were histogram-uniformity, GLCM-energy, and GLCM-homogeneity (-0.958, -0.958, and -0.957, respectively).A significant difference was observed in median SUVmax across clinical prognostic stages (p: 0.009). Stage IIIb patients had the highest SUVmax, histogram-entropy, GLCM-entropy, GLCM-contrast, and GLCM-dissimilarity values. The highest histogram-uniformity and GLCM-homogeneity were seen in patients with stage Ia disease.While there was no significant difference between SUVmax and lymph node metastasis, the median values of histogram-uniformity, GLCM-energy, and GLZLM-SZLGE were significantly higher in patients with lymph node metastasis (p=0.049, 0.048, and 0.010, respectively). The logistic regression analysis showed that GLZLM-SZLGE was the only variable that was independently associated with lymph node metastasis (Log OR: 20.4, 95% CI 4.23–36.62, p=0.013, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).DiscussionIn this study, we investigated the relationship between TFs extracted from 18F-FDG PET/CT and IHCs in IDBC. The logistic regression analysis showed that certain TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and high proliferation. While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE had an independent association with lymph node metastasis. Radiomics studies evaluating TFs represent many different biological factors such as tumor heterogeneity, histological architecture, cell proliferation, angiogenesis, and necrosis and can provide information about tumor prognosis, aggressiveness, and response to treatment.<a class="elsevierStyleCrossRefs" href="#bib0045">9,19–21</a> However, to date, they could not be transferred to clinical practice due to a lack of evidence in demonstrating the significance of TFs compared to SUV based parameters. From this perspective, we sought to investigate their contributory role in a homogeneous patient population with IDBC.In our previous study, we have found that metabolic parameters were higher in ER-negative, PR-negative, and also in HER-2-positive tumors.<a class="elsevierStyleCrossRef" href="#bib0110">22</a> Similar results were obtained in the present study. Also, we evaluated the relationship of TFs with prognostic factors and observed independent associations between ER-negativity, PR-negativity, and HER-2-positivity and TFs (GLCM-contrast, GLZLM-GLNU, and histogram-uniformity respectively). Similar to our work, Acar et al.<a class="elsevierStyleCrossRef" href="#bib0105">21</a> reported that ER/PR receptor-negative tumors were more heterogeneous according to TFs. On the other hand, Moscoso et al.<a class="elsevierStyleCrossRef" href="#bib0075">15</a> reported no significant difference between ER, PR, and HER-2 receptor status in terms of TFs.Several studies<a class="elsevierStyleCrossRefs" href="#bib0105">21,22</a> reported higher SUVmax values in high-grade tumors. In agreement with this, patients with high-grade tumors had higher SUVmax than those with low-grade ones in the present study. Huang et al.<a class="elsevierStyleCrossRef" href="#bib0115">23</a> found a positive correlation between grade and histogram-entropy, GLCM-dissimilarity, and GLCM-entropy, as well as a negative correlation between grade and histogram-uniformity, GLCM-energy, and GLCM-homogeneity. In our study, although most of the TFs (33/37) were significantly different in high-grade tumors compared with low-grade ones, only SUVmax was independently associated with high-grade. In line with previous reports,<a class="elsevierStyleCrossRefs" href="#bib0105">21,22,24</a> the Ki-67 proliferation index had a moderate positive correlation with SUVmax. Also, SUVmax was significantly higher in patients with a Ki-67 proliferation index of at least 14%, compared with those with lower values. The multivariate logistic regression model revealed that shape sphericity was an independent predictor of the highly proliferative tumors.Several studies showed that patients with Lum A subtype tumors had lower glycolytic activity than other subtypes.<a class="elsevierStyleCrossRefs" href="#bib0110">22,24,25</a> Similarly, in the present study, the median SUVmax was the lowest in the Lum A subtype and the highest in the TN subtype. According to multivariate logistic regression analysis, SUVmax had an independent association with triple-negativity. Several studies have demonstrated that tumor heterogeneity had a significant relationship with molecular subtypes.<a class="elsevierStyleCrossRefs" href="#bib0075">15,21,26</a> In line with the previous studies,<a class="elsevierStyleCrossRefs" href="#bib0105">21,26</a> median values of homogeneity-related TFs (histogram-uniformity and GLCM-homogeneity) were higher in the Lum A group and lower in the TN group. Moreover, the TN group had higher median values of heterogeneity-related TFs (GLCM-contrast, histogram-entropy, GLCM-entropy, and GLCM-dissimilarity). These findings suggest that Lum A group of tumors are more homogeneous and TN group tumors are more heterogeneous.Different results have been reported in studies examining the relationship between metabolic parameters and axillary lymph node status. Groheux et al.<a class="elsevierStyleCrossRef" href="#bib0135">27</a> did not find an association between SUVmax or other metabolic parameters and axillary lymph node metastasis. On the other hand, Kajáry et al.<a class="elsevierStyleCrossRef" href="#bib0120">24</a> found that SUVmax and other metabolic parameters of the tumor were associated with axillary lymph node involvement. The present study showed that patients with and without axillary lymph node involvement had similar SUVmax values.<a class="elsevierStyleCrossRef" href="#bib0110">22</a> Also in the present study, we found that histogram-uniformity, GLCM-energy, and GLZLM-SZLGE median values were significantly higher in the group with lymph node metastasis. We have also found that GLZLM-SZLGE, a high-order TF that shows the distribution of short homogeneous regions with low-grey-levels, was an independent predictor of axillary nodal involvement. While some TFs that represent heterogeneity (histogram-entropy, GLCM-entropy, GLCM-contrast, and GLCM-dissimilarity) were higher in stage IIIb patients, those that represent homogeneities such as histogram-homogeneity and GLCM-homogeneity were higher in stage Ia tumors. Besides this, the primary tumor SUVmax values were found to be higher as the stage of the disease increased.We found very strong positive and negative correlations between SUVmax and TFs. For instance, direct homogeneity-related TFs, such as histogram-uniformity and GLCM-homogeneity had strong negative correlations, while direct heterogeneity-related TFs such as histogram-entropy, GLCM-entropy, GLCM-contrast, and GLCM-dissimilarity had strong positive correlations. However, other studies<a class="elsevierStyleCrossRefs" href="#bib0065">13,28,29</a> did not find a significant relationship between SUV measurements and TFs and they reported strong correlations between TFs and metabolic tumor volume. These differences may result from heterogeneous patient populations and/or using different methods to obtain TFs.Among the limitations of the present study are the small sample size, single-center experience, and retrospective design. We cannot extrapolate the findings of the present study to patients with advanced-stage BC. Besides, TFs have not been accepted to be accurate enough to replace the IHC tests because the predictive specificity and accuracy of TFs have not been adequately investigated. Combination of TFs and metabolic parameters can be investigated as a potential approach to evaluate their contributory roles. Further studies are needed to show whether texture analysis with other PET tracers that reflect different aspects of tumor biology may produce similar results.ConclusionAccording to our results, ER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were more glycolytic and metabolically heterogeneous. These findings suggest that using TFs in addition to SUVmax for evaluating the aggressiveness of IDBC may improve the prognostic value of 18F-FDG PET/CT. TFs may be potential biomarkers in early-stage IDBC, as certain TFs were independently associated with many prognostic factors and predicted axillary lymph node involvement." "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres1640369" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objectıves" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1462582" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1640370" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Materiales y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1462581" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Inclusion and exclusion criteria" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "F-FDG PET/CT imaging protocol" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Image analysis and tumor segmentation" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Histopathologic evaluation" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0040" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0045" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0050" "titulo" => "Conclusion" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2020-07-26" "fechaAceptado" => "2020-10-18" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1462582" "palabras" => array:5 [ 0 => "Breast cancer" 1 => "Fluoride-18 fluorodeoxyglucose" 2 => "Positron emission tomography/Computed tomography" 3 => "Textural features" 4 => "Tumor heterogeneity" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1462581" "palabras" => array:5 [ 0 => "Cáncer de mama" 1 => "Fluoruro-18 Fluorodesoxiglucosa" 2 => "Tomografía por emisión de positrones/tomografía computarizada" 3 => "Características de textura" 4 => "Heterogeneidad tumoral" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "ObjectıvesThis study investigates whether textural features (TFs) extracted from 18F-FDG positron emission tomography/computed tomography (PET/CT) are associated with immunohistochemical characteristics (IHCs) of invasive ductal breast carcinoma (IDBC). Materials and methodsThe relationship of TFs with IHCs [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), Ki-67 proliferation index, and histological grades] from solely excised primary tumors were evaluated for a more accurate assessment. Therefore patients with early-stage IDBC who underwent pre-operative 18F-FDG PET/CT scan for staging were included in this retrospective study. The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer. Maximum standardized uptake value (SUVmax) and 37TFs of the primary tumor were extracted from 18F-FDG PET/CT. Spearman’s rank correlation test was used to evaluate the correlation between TFs and SUVmax. Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter. Among these parameters, those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics. ResultsA total of 124 patients were included. Histogram-uniformity, grey-level co-occurrence matrix (GLCM), GLCM-energy, and GLCM-homogeneity showed a strong negative correlation with SUVmax, while grey-level run-length matrix (GLRLM), GLRLM-SRHGE, grey-level zone length matrix (GLZLM), GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity showed a strong positive correlation. Some of the TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and increased Ki-67 proliferation index (GLCM-contrast, GLZLM-GLNU, histogram-uniformity, and shape-sphericity respectively). While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE, a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels, had an independent association with axillary lymph node metastasis. ConclusionsER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were found to be more glycolytic and metabolically heterogeneous. These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of 18F-FDG PET/CT in IDBC, as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objectıves" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "ObjetivoEste estudio investiga si las características de textura (TF) extraídas de la tomografía por emisión de positrones/tomografía computarizada (PET/TC) con fluoruro-18 fluorodesoxiglucosa (F-18 FDG) están asociadas con las características inmunohistoquímicas (IHC) del carcinoma ductal de mama invasivo (IDBC). Materiales y métodosSe evaluó la relación de TF con IHC [receptor de estrógeno (ER), receptor de progesterona (PR), receptor 2 del factor de crecimiento epidérmico humano (HER-2), índice de proliferación Ki-67 y grados histológicos] de tumores primarios extirpados únicamente para una evaluación más precisa. Por lo tanto, los pacientes con IDBC en estadio temprano que se sometieron a una exploración por PET/TC con F-18 FDG pre-operatoria para la estadificación se incluyeron en este estudio retrospectivo. La estadificación clínica se realizó de acuerdo con la 8a edición del American Joint Committee on Cancer. El valor máximo de captación estandarizada (SUVmáx) y 37 TF del tumor primario se extrajeron de F-18 FDG PET/TC. Se utilizó la prueba de correlación de rango de Spearman para evaluar la correlación entre TF y SUVmáx. Se generaron curvas de características operativas del receptor para definir el rendimiento diagnóstico de cada parámetro. Entre estos parámetros, aquellos con mayor rendimiento diagnóstico se incluyeron en el modelo de regresión logística multivariante para identificar los predictores independientes de las características histopatológicas. ResultadosSe incluyeron un total de 124 pacientes. La uniformidad del histograma, la energía GLCM y la homogeneidad GLCM mostraron una fuerte correlación negativa con SUVmax, mientras que GLRLM-SRHGE, GLZLM-HGZE, GLRLM-HGRE, GLCM-entropía, GLCM-contraste, histograma-entropía y GLCM-disimilitud mostraron una fuerte correlación positiva. Algunos de los TF se asociaron de forma independiente con ER-negatividad, PR-negatividad, HER-2-positividad y aumento del índice de proliferación de Ki-67 (GLCM-contraste, GLZLM-GLNU, histograma-uniformidad y forma-esfericidad respectivamente). Mientras que SUVmax tuvo una asociación independiente con alto grado y triple negatividad, GLZLM-SZLGE, un TF de alto orden que muestra la distribución de las zonas homogéneas cortas con niveles de gris bajos, tuvo una asociación independiente con metástasis en los ganglios linfáticos axilares. ConclusionesSe encontró que los tumores ER negativos, PR negativos, HER-2 positivos, triple negativos, de alto grado, altamente proliferativos y en estadio alto eran más glucolíticos y metabólicamente heterogéneos. Estos hallazgos sugieren que el uso de TF además de SUVmax puede mejorar el valor pronóstico de F-18 FDG PET/TC en IDBC, ya que ciertas TF se asociaron independientemente con muchas IHC y predijeron la afectación de los ganglios linfáticos axilares." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Materiales y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Önner H, Coskun N, Erol M, Eren Karanis Mİ. Asociación de características texturales de PET/TC 18F-FDG con características inmunohistoquímicas en el cáncer de mama ductal invasivo. Rev Esp Med Nucl Imagen Mol. 2022;41:11–16." ] ] "multimedia" => array:4 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 822 "Ancho" => 2500 "Tamanyo" => 93265 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Example of some textural features and SUVmax in a patient with luminal A. A shows transaxial PET/CT images, B represents grey-level distribution and C displays the image of a 3D extracted luminal A subtype tumor, SUVmax: 4.25, histogram-uniformity: 0.30, GLCM-contrast: 0.87, GLCM-entropy: 2.95, GLCM-energy: 0.05." ] ] 1 => array:8 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 827 "Ancho" => 2500 "Tamanyo" => 97521 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Example of some textural features and SUVmax in a patient with triple-negative subtype. A shows transaxial PET/CT images, B represents grey-level distribution and C displays the image of a 3D extracted triple-negative subtype tumor, SUVmax: 16.41, histogram-uniformity: 0.14, GLCM-contrast: 2.14, GLCM-entropy: 4.80, GLCM-energy: 0.17." ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0035" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "Data are represented as median and range or n (%).ER, Estrogen Receptor; HER-2, Human Epidermal Growth Factor Receptor 2; HER-2+, HER-2-positive; Lum A, luminal A like; Lum B-, luminal B like/HER2 negative; Lum B+, luminal B like/HER2-positive; PR, Progesterone Receptor; TN, triple-negative." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Age \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">53 (range: 29−85) \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">ER \t\t\t\t\t\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"> \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">Positive \t\t\t\t\t\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 (80.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Negative \t\t\t\t\t\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 (19.4%) \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">PR \t\t\t\t\t\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"> \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">Positive \t\t\t\t\t\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">86 (69.4%) \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">Negative \t\t\t\t\t\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">38 (30.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HER-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"> \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">Positive \t\t\t\t\t\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 (29.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Negative \t\t\t\t\t\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">87 (70.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Ki-67 Proliferative Index \t\t\t\t\t\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"> \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"><14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">38 (30.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">≥14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86 (69.4%) \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">Grade \t\t\t\t\t\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"> \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">I \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (11.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">II \t\t\t\t\t\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 (52.4%) \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">III \t\t\t\t\t\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 (36.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Molecular subtypes \t\t\t\t\t\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"> \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">Lum A \t\t\t\t\t\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 (25%) \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">Lum B - \t\t\t\t\t\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 (33.9%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Lum B + \t\t\t\t\t\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 (22.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HER-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">9 (7.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TN \t\t\t\t\t\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 (11.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Axillary lymph node involvement \t\t\t\t\t\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"> \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">Positive \t\t\t\t\t\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">84 (67.7%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Negative \t\t\t\t\t\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 (32.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TNM stage \t\t\t\t\t\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"> \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">IA \t\t\t\t\t\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 (5.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IB \t\t\t\t\t\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 (22.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IIA \t\t\t\t\t\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 (32.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IIB \t\t\t\t\t\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 (13.7%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IIIA \t\t\t\t\t\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 (14.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IIIB \t\t\t\t\t\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 (11.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2794663.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Patients’ characteristics." ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0040" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "AUC, the area under the curve; OR, odds ratio; CI; confidence interval, GLCM, gray-level co-occurrence matrix; GLZLM, gray-level zone length matrix; GLNU, gray level non-uniformity; SZLGE, short-zone low gray-level emphasis; SUVmax, maximum standardized uptake value; ER, estrogen receptor; PR, progesterone receptor; HER-2, human epidermal growth factor receptor-2." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \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">Predictive Parameter \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 \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">OR \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">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ER-negativity \t\t\t\t\t\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">GLCM-Contrast \t\t\t\t\t\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">0.755 \t\t\t\t\t\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">0.726 \t\t\t\t\t\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">0.561 – 0.939 \t\t\t\t\t\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">0.015 \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">PR-negativity \t\t\t\t\t\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">GLZLM-GLNU \t\t\t\t\t\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">0.706 \t\t\t\t\t\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">0.9 \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">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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HER-2-positivity \t\t\t\t\t\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">Histogram-uniformity \t\t\t\t\t\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">0.659 \t\t\t\t\t\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">0.028 \t\t\t\t\t\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">0.001 – 0.683 \t\t\t\t\t\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">0.02 \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">Triple-negativity \t\t\t\t\t\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">SUVmax \t\t\t\t\t\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">0.776 \t\t\t\t\t\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">1.13 \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">1.01 – 1.26 \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"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">High-grade \t\t\t\t\t\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">SUVmax \t\t\t\t\t\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">0.749 \t\t\t\t\t\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">1.126 \t\t\t\t\t\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">1.06 – 1.18 \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"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">High-proliferation \t\t\t\t\t\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">Shape-sphericity<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\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">0.706 \t\t\t\t\t\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">13.80 \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">1.57 – 26.04 \t\t\t\t\t\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">0.027 \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">Lymph node involvement \t\t\t\t\t\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">GLZLM-SZLGE<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\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">0.640 \t\t\t\t\t\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">20.4 \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">4.23 – 36.62 \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">0.013 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2794662.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Analyzed on log scale." ] ] ] "descripcion" => array:1 [ "en" => "Predictive parameters for prognostic factors in multivariate logistic regression analysis." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:29 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Cancer heterogeneity: implications for targeted therapeutics" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "R. 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Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer

Asociación de características texturales de PET/TC 18F-FDG con características inmunohistoquímicas en el cáncer de mama ductal invasivo

Hasan Önnera,

Corresponding author

hasanonner_1988@hotmail.com

Corresponding author.

, Nazim Coskunb, Mustafa Erola, Meryem İlkay Eren Karanisa

a Konya City Hospital, Konya, Turkey

b Ankara City Hospital, Ankara, Turkey

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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Cancers are heterogeneous cell mixtures that differ in their morphology&#44; genetics&#44; and biological behavior&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> Heterogeneity is one of the main features of cancer biology and is associated with worse prognosis due to aggressive biological behavior and treatment failures&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> Breast cancer &#40;BC&#41; is a highly heterogeneous type of cancer&#44; and various factors such as estrogen receptor &#40;ER&#41;&#44; progesterone receptor &#40;PR&#41;&#44; and human epidermal growth factor receptor-2 &#40;HER-2&#41; status are responsible for tumor heterogeneity&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> Apart from these&#44; many factors such as tumor hypoxia&#44; angiogenesis&#44; necrosis&#44; fibrosis&#44; and cell proliferation play a role in tumor heterogeneity&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> However&#44; tumor heterogeneity cannot be determined precisely as biopsy samples obtained only from an area of the tumor cannot reflect the exact size of the phenotypic and genetic variations&#46;<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2&#44;4</span></a> Therefore&#44; an accurate diagnosis is attempted by taking biopsy samples from more than one region of the tumor&#46; However&#44; more aggressive areas may still be ignored due to heterogeneity&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> It is an important requirement to demonstrate the heterogeneity of the entire tumor noninvasively&#44; to understand its nature&#44; to predict prognostic factors&#44; and to plan treatment management&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Fluoride-18 fluorodeoxyglucose &#40;<span class="elsevierStyleSup">18</span>F-FDG&#41; positron emission tomography&#47;computed tomography &#40;PET&#47;CT&#41; is widely used in BC for staging&#44; recurrence detection&#44; and assessing response to treatment&#46; Standardized uptake value &#40;SUV&#41; is a semi-quantitative parameter that providing prognostic information by showing the degree of <span class="elsevierStyleSup">18</span>F-FDG uptake&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> Among the SUV parameters&#44; the most commonly used maximum standardized uptake value &#40;SUVmax&#41; expresses the single voxel value representing the most intense <span class="elsevierStyleSup">18</span>F-FDG uptake in the tumor&#44; and cannot accurately reflect glucose metabolism especially in heterogeneous tumors such as BC&#46; It is also influenced by various factors such as body weight&#44; plasma glucose level&#44; partial volume effects&#44; and time of injection&#46;<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7&#44;8</span></a> For these reasons&#44; novel parameters are needed to better understand tumor heterogeneity and to guide clinicians for personalized treatments&#46; Texture analysis consists of several mathematical methods that describe the relationship between the grey-level intensity of voxels and their positions within a defined volume of interest&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> Briefly&#44; it is used to measure tumor heterogeneity by enabling the tumors to be visualized as granulate or rough&#46; In recent years&#44; interest in tumor texture analysis by <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT scan has been increased in the field of oncology&#46; In transgenic mice with orthotopically implanted breast tumors&#44; heterogeneity calculated in-vivo from PET images accurately reflects the heterogeneity of ex-vivo tracer uptake calculated directly from autoradiographic images&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> Also&#44; several studies on different types of tumors have found that texture analysis performs better in distinguishing tumor histology&#44; predicting treatment response&#44; and assessing correlation with prognostic factors&#46;<a class="elsevierStyleCrossRefs" href="#bib0055"><span class="elsevierStyleSup">11&#8211;15</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In this context&#59; this study aims to investigate the relationship between primary tumor textural features &#40;TFs&#41; extracted from <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT and IHCs&#44; and to evaluate the complementary role of TFs for evaluating the aggressiveness of invasive ductal breast cancer &#40;IDBC&#41;&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Inclusion and exclusion criteria</span><p id="par0020" class="elsevierStylePara elsevierViewall">This retrospective study was approved by the Institutional Review Board &#40;Approval No&#58; 41901325-050&#46;99&#41;&#46; Written informed consent was obtained from all patients who underwent PET&#47;CT for clinical indications&#44; accepting that their data could be used in clinical trials&#46; Patients with IDBC who underwent <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT exams before any surgery and&#47;or any therapeutic interventions between June 2017 and December 2019 were included&#46; Exclusion criteria were as follows&#58; those with tumors smaller than 1&#46;5<span class="elsevierStyleHsp" style=""></span>cm &#40;to minimize the partial volume effect&#41;&#44; those with tumors less than 64 pixels of grey-level &#40;because advanced texture analysis is not obtainable&#41;&#44; and those with local advanced or advanced-stage tumors &#40;requiring pre-operative treatment before surgery&#41;&#46; Patients who did not bear these exclusion criteria were included consecutively&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080"><span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT imaging protocol</span><p id="par0025" class="elsevierStylePara elsevierViewall">Since the blood glucose level should be less than 200<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#44; patients fasted for at least four hours before the examination&#46; A Siemens Biograph LSO-16 PET&#47;CT scanner &#40;Siemens Medical Solutions&#44; Chicago&#44; IL&#41; was used&#46; <span class="elsevierStyleSup">18</span>F-FDG was intravenously administered at a dose of 236&#46;8&#8211;510&#46;6<span class="elsevierStyleHsp" style=""></span>MBq &#40;6&#46;4&#8211;13&#46;8<span class="elsevierStyleHsp" style=""></span>mCi&#41; according to body weight &#40;3&#46;7<span class="elsevierStyleHsp" style=""></span>MBq&#47;kg&#41;&#46; Computed tomography &#40;CT&#41; was performed on a 4-slice spiral CT using a slice thickness of 5<span class="elsevierStyleHsp" style=""></span>mm &#40;120&#8211;150<span class="elsevierStyleHsp" style=""></span>kV&#44; 80<span class="elsevierStyleHsp" style=""></span>mA&#41;&#46; After the transmission scan&#44; 3D PET acquisition was obtained by six to eight two minute sessions in the bed position&#46; For attenuation correction of PET data&#44; CT images were used&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Image analysis and tumor segmentation</span><p id="par0030" class="elsevierStylePara elsevierViewall">The images were visually assessed by two experienced nuclear medicine specialists in a consensus&#46; Primary breast tumors on PET&#47;CT images were delineated and SUVmax and TFs were calculated through PET images using the LIFEx software package program &#40;Orsay&#44; France&#41; &#40;10&#41;&#46; Second and higher-order imaging parameters were extracted only for lesions greater than 64 voxels&#46; The first-order TFs which were obtained for this study included histogram and shape-based features&#46; In the further textural analysis&#44; grey-level co-occurrence matrix &#40;GLCM&#41;&#44; grey-level run-length matrix &#40;GLRLM&#41;&#44; neighborhood grey-level different matrix &#40;NGLDM&#41;&#44; and grey-level zone length matrix &#40;GLZLM&#41; parameters were obtained&#46; As a result&#44; SUVmax and 37<span class="elsevierStyleHsp" style=""></span>TFs of primary breast tumor were extracted&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Histopathologic evaluation</span><p id="par0035" class="elsevierStylePara elsevierViewall">All included patients underwent clinically selected appropriate surgical treatment &#40;partial mastectomy or transverse rectus abdominal flap reconstruction and skin-sparing mastectomy&#44; sentinel lymph node biopsy&#44; or axillary lymph node dissection&#41; in a median duration of 23 &#40;13&#8211;46&#41; days after <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT imaging&#46; All IHCs were evaluated from surgical samples&#46; Both proportional staining &#40;&#37;&#41; and densities &#40;1&#43;&#44; 2&#43;&#44; and 3&#43;&#41; were taken into account to evaluate the ER and PR status of tumor cells&#46; HER-2 gene expression status was considered positive when membrane immune staining was 3&#43;&#44; negative when it was 1&#43; or no staining was seen&#46; The fluorescent in situ hybridization &#40;FISH&#41; method was used to confirm the presence of HER-2 gene expression when scores were 2<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>&#46; The Scarff Bloom Richardson classification system was used to evaluate histopathological grade&#46;<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">15&#44;16</span></a> The patients with grade I or II tumors were classified as having low-grade and those with grade III tumors were classified as having high-grade tumors&#46; The Ki-67 proliferation index was evaluated according to hot spot areas&#46; In addition&#44; lymph node metastasis status and tumor diameter were obtained from post-operative pathology reports&#46; The clinical prognostic staging was accompanied by histopathological findings&#44; as reported in the American Cancer Joint Committee Cancer Staging Manual&#44; 8th edition&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> The tumors were classified into the molecular subtypes<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> as follows&#58;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">1</span><p id="par0040" class="elsevierStylePara elsevierViewall">Luminal A &#40;Lum A&#41;&#58; ER-positive and&#47;or PR-positive&#44; HER-2-negative&#44; and a Ki-67 proliferation index less than 14&#37;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">2</span><p id="par0045" class="elsevierStylePara elsevierViewall">Luminal B HER-2 negative &#40;Lum B-&#41;&#58; ER-positive and&#47;or PR-positive&#44; HER-2-negative&#44; and a Ki-67 proliferation index at least 14&#37;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">3</span><p id="par0050" class="elsevierStylePara elsevierViewall">Luminal B HER-2 positive &#40;Lum B&#43;&#41;&#58; ER-positive and&#47;or PR-positive&#44; HER-2-positive&#44; and any Ki-67 proliferation index&#46;</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">4</span><p id="par0055" class="elsevierStylePara elsevierViewall">HER-2 positive&#58; ER-negative&#44; PR-negative&#44; HER-2-positive&#46;</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">5</span><p id="par0060" class="elsevierStylePara elsevierViewall">Triple-negative &#40;TN&#41;&#58; ER-negative&#44; PR-negative&#44; HER-2-negative&#46;</p></li></ul></p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Statistical analysis</span><p id="par0065" class="elsevierStylePara elsevierViewall">For statistical analyses&#44; IBM SPSS Statistics for Windows&#44; Version 21&#46;0 &#40;IBM Corp&#46;&#44; Armonk&#44; NY&#41; was used&#46; Nonparametric tests were used in all comparisons because of the non-homogeneity of the data&#46; Spearman&#39;s rank correlation test was used to evaluate the correlation between TFs and SUVmax&#46; The diagnostic performance of quantitative parameters for predicting prognostic features were evaluated with the receiver operating characteristic &#40;ROC&#41; curves&#46; For multivariate analysis&#44; the area under the curve &#40;AUC&#41; for each TF was evaluated and those with the highest AUC in their respective category were included in multivariate logistic regression models&#46; Thus&#44; variables independently related to ER-negativity&#44; PR-negativity&#44; HER-2-positivity&#44; lymph node metastasis&#44; high-grade&#44; increased Ki-67 proliferation index&#44; and triple-negativity were sought&#46; Power of estimation was Hosmer-Lemeshow goodness of fit statistics was used for the evaluation of model fit&#46;</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Results</span><p id="par0070" class="elsevierStylePara elsevierViewall">The study population consisted of 124 patients &#40;median age 53&#44; range 29&#8211;85 years&#41;&#46; Fourteen patients &#40;11&#46;3&#37;&#41; had grade I&#44; 65 &#40;52&#46;4&#37;&#41; had grade II&#44; and 45 &#40;36&#46;3&#37;&#41; had grade III tumors&#46; There were 34 &#40;30&#46;6&#37;&#41; tumors with a Ki-67 proliferation index of less than 14&#37;&#46; The positivity rates of ER&#44; PR&#44; and HER-2 were 100 &#40;80&#46;6&#37;&#41;&#44; 86 &#40;69&#46;4&#37;&#41;&#44; and 37 &#40;29&#46;8&#37;&#41;&#44; respectively&#46; The frequency of molecular subtypes were as follows&#59; 31 &#40;25&#37;&#41; patients had Lum A&#44; 42 &#40;33&#46;9&#37;&#41; had Lum B-&#44; 28 &#40;22&#46;6&#37;&#41; had Lum B&#43;&#44; 9 &#40;7&#46;2&#37;&#41; had HER-2&#43;&#44; and 14 &#40;11&#46;3&#37;&#41; had TN subtype&#46; Eighty-four &#40;67&#46;8&#37;&#41; patients had axillary lymph node metastasis&#46; Seven &#40;5&#46;6&#37;&#41; patients had stage IA&#44; 28 &#40;22&#46;6&#37;&#41; had stage IB&#44; 40 &#40;32&#46;3&#37;&#41; had stage IIA&#44; 17 &#40;13&#46;7&#37;&#41; had stage IIB&#44; 18 &#40;14&#46;5&#37;&#41; had stage IIIA&#44; and 14 &#40;11&#46;3&#37;&#41; had stage IIIB disease&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">The patients&#8217; characteristics are summarized in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46; <a class="elsevierStyleCrossRefs" href="#fig0005">Figs&#46; 1 and 2</a> illustrate PET&#47;CT and radiomics images of two different IDBC subtypes&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">The Median SUVmax value of ER-negative tumors &#40;17&#46;05&#41; was significantly higher than ER-positive ones &#40;7&#46;93&#41; &#40;p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#46; Significant differences were found in 30 of 37<span class="elsevierStyleHsp" style=""></span>TFs in terms of ER status&#46; Moreover&#44; GLCM-contrast was independently associated with ER-negativity in logistic regression analysis &#40;OR<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;726 95&#37; CI 0&#46;561&#8211;0&#46;939&#44; p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;015&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; The PR-negative tumors had significantly higher SUVmax values compared with PR-positive ones &#40;median&#58; 12&#46;84 vs 7&#46;43&#44; p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#46; Twenty-eight of 37<span class="elsevierStyleHsp" style=""></span>TFs showed a significant difference between PR-negative and PR-positive patients&#46; Logistic regression analysis revealed that GLZLM-GLNU was independently associated with PR-negativity with an OR of 0&#46;9 &#40;95&#37; CI 0&#46;85 &#8211; 0&#46;95&#44; p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; The tumors with HER-2 gene expression had higher metabolic activity than those without HER-2 gene expression &#40;median SUVmax&#58; 11&#46;62 vs&#46; 7&#46;72&#44; p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;009&#41;&#46; Also&#44; 21 out of 37<span class="elsevierStyleHsp" style=""></span>TFs were significantly different between HER-2-positive and HER-2-negative tumors&#46; Histogram-uniformity was independently associated with HER-2-positivity with an OR of 0&#46;028 &#40;95&#37; CI 0&#46;001 &#8211; 0&#46;683&#44; p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;02&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">A significant difference was observed in SUVmax values across different grade groups &#40;p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#46; When TFs were compared between the low-grade &#40;grade I and II&#41; and high-grade &#40;grade III&#41; groups&#44; a significant difference was observed in 31 of 37<span class="elsevierStyleHsp" style=""></span>TFs&#46; However&#44; the only variable that showed an independent association with a high-grade was SUVmax &#40;OR&#58; 1&#46;126&#44; 95&#37; CI 1&#46;06&#8211;1&#46;18&#44; p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; Ki-67 proliferation index was moderately correlated with SUVmax &#40;r&#58; 0&#46;464&#44; p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#46; The median SUVmax value was also significantly higher in tumors with an increased Ki-67 proliferation index &#40;11&#46;58 for Ki-67<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>14&#37; vs&#46; 5&#46;29 for Ki-67<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>14&#37;&#44; p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#46; The logistic regression model of the parameters with the highest AUC value revealed that shape sphericity was an independent predictor of high proliferation &#40;Log OR&#58; 13&#46;80&#44; 95&#37; CI 1&#46;57&#8211;26&#46;04&#44; p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;027&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">Molecular subtypes were associated with significantly different SUVmax values &#40;p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#46; The median SUVmax was the lowest in Lum A subtype &#40;5&#46;22&#44; range&#58; 1&#46;48&#8211;21&#46;62&#41; and the highest in TN &#40;17&#46;05 range&#58; 7&#46;72&#8211;42&#46;88&#41;&#46; Thirty-three out of 37<span class="elsevierStyleHsp" style=""></span>TFs showed a significant difference across molecular subtypes&#46; However&#44; only SUVmax was independently associated with triple-negativity &#40;OR&#58; 1&#46;13&#44; 95&#37; CI 1&#46;01&#8211;1&#46;26&#44; p<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">The TFs that showed a strong positive correlation &#40;r&#62;0&#46;95&#41; with SUVmax were GLRLM-SRHGE&#44; GLZLM-HGZE&#44; GLRLM-HGRE&#44; GLCM-entropy&#44; GLCM-contrast&#44; histogram-entropy&#44; and GLCM-dissimilarity &#40;0&#46;984&#44; 0&#46;984&#44; 0&#46;979&#44; 0&#46;973&#44; 0&#46;969&#44; 0&#46;968&#44; and 0&#46;965&#44; respectively&#41; while those which had a strong negative correlation &#40;r&#60;-0&#46;95&#41; with SUVmax were histogram-uniformity&#44; GLCM-energy&#44; and GLCM-homogeneity &#40;-0&#46;958&#44; -0&#46;958&#44; and -0&#46;957&#44; respectively&#41;&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">A significant difference was observed in median SUVmax across clinical prognostic stages &#40;p&#58; 0&#46;009&#41;&#46; Stage IIIb patients had the highest SUVmax&#44; histogram-entropy&#44; GLCM-entropy&#44; GLCM-contrast&#44; and GLCM-dissimilarity values&#46; The highest histogram-uniformity and GLCM-homogeneity were seen in patients with stage Ia disease&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">While there was no significant difference between SUVmax and lymph node metastasis&#44; the median values of histogram-uniformity&#44; GLCM-energy&#44; and GLZLM-SZLGE were significantly higher in patients with lymph node metastasis &#40;p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;049&#44; 0&#46;048&#44; and 0&#46;010&#44; respectively&#41;&#46; The logistic regression analysis showed that GLZLM-SZLGE was the only variable that was independently associated with lymph node metastasis &#40;Log OR&#58; 20&#46;4&#44; 95&#37; CI 4&#46;23&#8211;36&#46;62&#44; p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;013&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Discussion</span><p id="par0110" class="elsevierStylePara elsevierViewall">In this study&#44; we investigated the relationship between TFs extracted from <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT and IHCs in IDBC&#46; The logistic regression analysis showed that certain TFs were independently associated with ER-negativity&#44; PR-negativity&#44; HER-2-positivity&#44; and high proliferation&#46; While SUVmax had an independent association with high-grade and triple-negativity&#44; GLZLM-SZLGE had an independent association with lymph node metastasis&#46; Radiomics studies evaluating TFs represent many different biological factors such as tumor heterogeneity&#44; histological architecture&#44; cell proliferation&#44; angiogenesis&#44; and necrosis and can provide information about tumor prognosis&#44; aggressiveness&#44; and response to treatment&#46;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#44;19&#8211;21</span></a> However&#44; to date&#44; they could not be transferred to clinical practice due to a lack of evidence in demonstrating the significance of TFs compared to SUV based parameters&#46; From this perspective&#44; we sought to investigate their contributory role in a homogeneous patient population with IDBC&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">In our previous study&#44; we have found that metabolic parameters were higher in ER-negative&#44; PR-negative&#44; and also in HER-2-positive tumors&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> Similar results were obtained in the present study&#46; Also&#44; we evaluated the relationship of TFs with prognostic factors and observed independent associations between ER-negativity&#44; PR-negativity&#44; and HER-2-positivity and TFs &#40;GLCM-contrast&#44; GLZLM-GLNU&#44; and histogram-uniformity respectively&#41;&#46; Similar to our work&#44; Acar et al&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> reported that ER&#47;PR receptor-negative tumors were more heterogeneous according to TFs&#46; On the other hand&#44; Moscoso et al&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> reported no significant difference between ER&#44; PR&#44; and HER-2 receptor status in terms of TFs&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">Several studies<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21&#44;22</span></a> reported higher SUVmax values in high-grade tumors&#46; In agreement with this&#44; patients with high-grade tumors had higher SUVmax than those with low-grade ones in the present study&#46; Huang et al&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> found a positive correlation between grade and histogram-entropy&#44; GLCM-dissimilarity&#44; and GLCM-entropy&#44; as well as a negative correlation between grade and histogram-uniformity&#44; GLCM-energy&#44; and GLCM-homogeneity&#46; In our study&#44; although most of the TFs &#40;33&#47;37&#41; were significantly different in high-grade tumors compared with low-grade ones&#44; only SUVmax was independently associated with high-grade&#46; In line with previous reports&#44;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21&#44;22&#44;24</span></a> the Ki-67 proliferation index had a moderate positive correlation with SUVmax&#46; Also&#44; SUVmax was significantly higher in patients with a Ki-67 proliferation index of at least 14&#37;&#44; compared with those with lower values&#46; The multivariate logistic regression model revealed that shape sphericity was an independent predictor of the highly proliferative tumors&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">Several studies showed that patients with Lum A subtype tumors had lower glycolytic activity than other subtypes&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#44;24&#44;25</span></a> Similarly&#44; in the present study&#44; the median SUVmax was the lowest in the Lum A subtype and the highest in the TN subtype&#46; According to multivariate logistic regression analysis&#44; SUVmax had an independent association with triple-negativity&#46; Several studies have demonstrated that tumor heterogeneity had a significant relationship with molecular subtypes&#46;<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">15&#44;21&#44;26</span></a> In line with the previous studies&#44;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21&#44;26</span></a> median values of homogeneity-related TFs &#40;histogram-uniformity and GLCM-homogeneity&#41; were higher in the Lum A group and lower in the TN group&#46; Moreover&#44; the TN group had higher median values of heterogeneity-related TFs &#40;GLCM-contrast&#44; histogram-entropy&#44; GLCM-entropy&#44; and GLCM-dissimilarity&#41;&#46; These findings suggest that Lum A group of tumors are more homogeneous and TN group tumors are more heterogeneous&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">Different results have been reported in studies examining the relationship between metabolic parameters and axillary lymph node status&#46; Groheux et al&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a> did not find an association between SUVmax or other metabolic parameters and axillary lymph node metastasis&#46; On the other hand&#44; Kaj&#225;ry et al&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> found that SUVmax and other metabolic parameters of the tumor were associated with axillary lymph node involvement&#46; The present study showed that patients with and without axillary lymph node involvement had similar SUVmax values&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> Also in the present study&#44; we found that histogram-uniformity&#44; GLCM-energy&#44; and GLZLM-SZLGE median values were significantly higher in the group with lymph node metastasis&#46; We have also found that GLZLM-SZLGE&#44; a high-order TF that shows the distribution of short homogeneous regions with low-grey-levels&#44; was an independent predictor of axillary nodal involvement&#46; While some TFs that represent heterogeneity &#40;histogram-entropy&#44; GLCM-entropy&#44; GLCM-contrast&#44; and GLCM-dissimilarity&#41; were higher in stage IIIb patients&#44; those that represent homogeneities such as histogram-homogeneity and GLCM-homogeneity were higher in stage Ia tumors&#46; Besides this&#44; the primary tumor SUVmax values were found to be higher as the stage of the disease increased&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">We found very strong positive and negative correlations between SUVmax and TFs&#46; For instance&#44; direct homogeneity-related TFs&#44; such as histogram-uniformity and GLCM-homogeneity had strong negative correlations&#44; while direct heterogeneity-related TFs such as histogram-entropy&#44; GLCM-entropy&#44; GLCM-contrast&#44; and GLCM-dissimilarity had strong positive correlations&#46; However&#44; other studies<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#44;28&#44;29</span></a> did not find a significant relationship between SUV measurements and TFs and they reported strong correlations between TFs and metabolic tumor volume&#46; These differences may result from heterogeneous patient populations and&#47;or using different methods to obtain TFs&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">Among the limitations of the present study are the small sample size&#44; single-center experience&#44; and retrospective design&#46; We cannot extrapolate the findings of the present study to patients with advanced-stage BC&#46; Besides&#44; TFs have not been accepted to be accurate enough to replace the IHC tests because the predictive specificity and accuracy of TFs have not been adequately investigated&#46; Combination of TFs and metabolic parameters can be investigated as a potential approach to evaluate their contributory roles&#46; Further studies are needed to show whether texture analysis with other PET tracers that reflect different aspects of tumor biology may produce similar results&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conclusion</span><p id="par0145" class="elsevierStylePara elsevierViewall">According to our results&#44; ER-negative&#44; PR-negative&#44; HER-2-positive&#44; triple-negative&#44; high-grade&#44; highly proliferative&#44; and high-stage tumors were more glycolytic and metabolically heterogeneous&#46; These findings suggest that using TFs in addition to SUVmax for evaluating the aggressiveness of IDBC may improve the prognostic value of <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT&#46; TFs may be potential biomarkers in early-stage IDBC&#44; as certain TFs were independently associated with many prognostic factors and predicted axillary lymph node involvement&#46;</p></span></span>"
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Object&#305;ves</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">This study investigates whether textural features &#40;TFs&#41; extracted from <span class="elsevierStyleSup">18</span>F-FDG positron emission tomography&#47;computed tomography &#40;PET&#47;CT&#41; are associated with immunohistochemical characteristics &#40;IHCs&#41; of invasive ductal breast carcinoma &#40;IDBC&#41;&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">The relationship of TFs with IHCs &#91;estrogen receptor &#40;ER&#41;&#44; progesterone receptor &#40;PR&#41;&#44; human epidermal growth factor receptor-2 &#40;HER-2&#41;&#44; Ki-67 proliferation index&#44; and histological grades&#93; from solely excised primary tumors were evaluated for a more accurate assessment&#46; Therefore patients with early-stage IDBC who underwent pre-operative <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT scan for staging were included in this retrospective study&#46; The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer&#46; Maximum standardized uptake value &#40;SUVmax&#41; and 37<span class="elsevierStyleHsp" style=""></span>TFs of the primary tumor were extracted from <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT&#46; Spearman&#8217;s rank correlation test was used to evaluate the correlation between TFs and SUVmax&#46; Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter&#46; Among these parameters&#44; those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">A total of 124 patients were included&#46; Histogram-uniformity&#44; grey-level co-occurrence matrix &#40;GLCM&#41;&#44; GLCM-energy&#44; and GLCM-homogeneity showed a strong negative correlation with SUVmax&#44; while grey-level run-length matrix &#40;GLRLM&#41;&#44; GLRLM-SRHGE&#44; grey-level zone length matrix &#40;GLZLM&#41;&#44; GLZLM-HGZE&#44; GLRLM-HGRE&#44; GLCM-entropy&#44; GLCM-contrast&#44; histogram-entropy&#44; and GLCM-dissimilarity showed a strong positive correlation&#46; Some of the TFs were independently associated with ER-negativity&#44; PR-negativity&#44; HER-2-positivity&#44; and increased Ki-67 proliferation index &#40;GLCM-contrast&#44; GLZLM-GLNU&#44; histogram-uniformity&#44; and shape-sphericity respectively&#41;&#46; While SUVmax had an independent association with high-grade and triple-negativity&#44; GLZLM-SZLGE&#44; a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels&#44; had an independent association with axillary lymph node metastasis&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">ER-negative&#44; PR-negative&#44; HER-2-positive&#44; triple-negative&#44; high-grade&#44; highly proliferative&#44; and high-stage tumors were found to be more glycolytic and metabolically heterogeneous&#46; These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT in IDBC&#44; as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0005"
            "titulo" => "Object&#305;ves"
          ]
          1 => array:2 [
            "identificador" => "abst0010"
            "titulo" => "Materials and methods"
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          2 => array:2 [
            "identificador" => "abst0015"
            "titulo" => "Results"
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          3 => array:2 [
            "identificador" => "abst0020"
            "titulo" => "Conclusions"
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      "es" => array:3 [
        "titulo" => "Resumen"
        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Este estudio investiga si las caracter&#237;sticas de textura &#40;TF&#41; extra&#237;das de la tomograf&#237;a por emisi&#243;n de positrones&#47;tomograf&#237;a computarizada &#40;PET&#47;TC&#41; con fluoruro-18 fluorodesoxiglucosa &#40;F-18 FDG&#41; est&#225;n asociadas con las caracter&#237;sticas inmunohistoqu&#237;micas &#40;IHC&#41; del carcinoma ductal de mama invasivo &#40;IDBC&#41;&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Materiales y m&#233;todos</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Se evalu&#243; la relaci&#243;n de TF con IHC &#91;receptor de estr&#243;geno &#40;ER&#41;&#44; receptor de progesterona &#40;PR&#41;&#44; receptor 2 del factor de crecimiento epid&#233;rmico humano &#40;HER-2&#41;&#44; &#237;ndice de proliferaci&#243;n Ki-67 y grados histol&#243;gicos&#93; de tumores primarios extirpados &#250;nicamente para una evaluaci&#243;n m&#225;s precisa&#46; Por lo tanto&#44; los pacientes con IDBC en estadio temprano que se sometieron a una exploraci&#243;n por PET&#47;TC con F-18 FDG pre-operatoria para la estadificaci&#243;n se incluyeron en este estudio retrospectivo&#46; La estadificaci&#243;n cl&#237;nica se realiz&#243; de acuerdo con la 8a edici&#243;n del American Joint Committee on Cancer&#46; El valor m&#225;ximo de captaci&#243;n estandarizada &#40;SUVm&#225;x&#41; y 37 TF del tumor primario se extrajeron de F-18 FDG PET&#47;TC&#46; Se utiliz&#243; la prueba de correlaci&#243;n de rango de Spearman para evaluar la correlaci&#243;n entre TF y SUVm&#225;x&#46; Se generaron curvas de caracter&#237;sticas operativas del receptor para definir el rendimiento diagn&#243;stico de cada par&#225;metro&#46; Entre estos par&#225;metros&#44; aquellos con mayor rendimiento diagn&#243;stico se incluyeron en el modelo de regresi&#243;n log&#237;stica multivariante para identificar los predictores independientes de las caracter&#237;sticas histopatol&#243;gicas&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Se incluyeron un total de 124 pacientes&#46; La uniformidad del histograma&#44; la energ&#237;a GLCM y la homogeneidad GLCM mostraron una fuerte correlaci&#243;n negativa con SUVmax&#44; mientras que GLRLM-SRHGE&#44; GLZLM-HGZE&#44; GLRLM-HGRE&#44; GLCM-entrop&#237;a&#44; GLCM-contraste&#44; histograma-entrop&#237;a y GLCM-disimilitud mostraron una fuerte correlaci&#243;n positiva&#46; Algunos de los TF se asociaron de forma independiente con ER-negatividad&#44; PR-negatividad&#44; HER-2-positividad y aumento del &#237;ndice de proliferaci&#243;n de Ki-67 &#40;GLCM-contraste&#44; GLZLM-GLNU&#44; histograma-uniformidad y forma-esfericidad respectivamente&#41;&#46; Mientras que SUVmax tuvo una asociaci&#243;n independiente con alto grado y triple negatividad&#44; GLZLM-SZLGE&#44; un TF de alto orden que muestra la distribuci&#243;n de las zonas homog&#233;neas cortas con niveles de gris bajos&#44; tuvo una asociaci&#243;n independiente con met&#225;stasis en los ganglios linf&#225;ticos axilares&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Se encontr&#243; que los tumores ER negativos&#44; PR negativos&#44; HER-2 positivos&#44; triple negativos&#44; de alto grado&#44; altamente proliferativos y en estadio alto eran m&#225;s glucol&#237;ticos y metab&#243;licamente heterog&#233;neos&#46; Estos hallazgos sugieren que el uso de TF adem&#225;s de SUVmax puede mejorar el valor pron&#243;stico de F-18 FDG PET&#47;TC en IDBC&#44; ya que ciertas TF se asociaron independientemente con muchas IHC y predijeron la afectaci&#243;n de los ganglios linf&#225;ticos axilares&#46;</p></span>"
        "secciones" => array:4 [
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            "identificador" => "abst0025"
            "titulo" => "Objetivo"
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          1 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "Materiales y m&#233;todos"
          ]
          2 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Resultados"
          ]
          3 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Conclusiones"
          ]
        ]
      ]
    ]
    "NotaPie" => array:1 [
      0 => array:2 [
        "etiqueta" => "&#9734;"
        "nota" => "<p class="elsevierStyleNotepara" id="npar9005">Please cite this article as&#58; &#214;nner H&#44; Coskun N&#44; Erol M&#44; Eren Karanis M&#304;&#46; Asociaci&#243;n de caracter&#237;sticas texturales de PET&#47;TC 18F-FDG con caracter&#237;sticas inmunohistoqu&#237;micas en el c&#225;ncer de mama ductal invasivo&#46; Rev Esp Med Nucl Imagen Mol&#46; 2022&#59;41&#58;11&#8211;16&#46;</p>"
      ]
    ]
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        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 822
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        "descripcion" => array:1 [
          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Example of some textural features and SUVmax in a patient with luminal A&#46; A shows transaxial PET&#47;CT images&#44; B represents grey-level distribution and C displays the image of a 3D extracted luminal A subtype tumor&#44; SUVmax&#58; 4&#46;25&#44; histogram-uniformity&#58; 0&#46;30&#44; GLCM-contrast&#58; 0&#46;87&#44; GLCM-entropy&#58; 2&#46;95&#44; GLCM-energy&#58; 0&#46;05&#46;</p>"
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      1 => array:8 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr2.jpeg"
            "Alto" => 827
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            "Tamanyo" => 97521
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0030"
            "detalle" => "Figure "
            "rol" => "short"
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Example of some textural features and SUVmax in a patient with triple-negative subtype&#46; A shows transaxial PET&#47;CT images&#44; B represents grey-level distribution and C displays the image of a 3D extracted triple-negative subtype tumor&#44; SUVmax&#58; 16&#46;41&#44; histogram-uniformity&#58; 0&#46;14&#44; GLCM-contrast&#58; 2&#46;14&#44; GLCM-entropy&#58; 4&#46;80&#44; GLCM-energy&#58; 0&#46;17&#46;</p>"
        ]
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      2 => array:8 [
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        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
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            "identificador" => "at0035"
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          "leyenda" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Data are represented as median and range or n &#40;&#37;&#41;&#46;</p><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">ER&#44; Estrogen Receptor&#59; HER-2&#44; Human Epidermal Growth Factor Receptor 2&#59; HER-2&#43;&#44; HER-2-positive&#59; Lum A&#44; luminal A like&#59; Lum B-&#44; luminal B like&#47;HER2 negative&#59; Lum B&#43;&#44; luminal B like&#47;HER2-positive&#59; PR&#44; Progesterone Receptor&#59; TN&#44; triple-negative&#46;</p>"
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Age&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">53 &#40;range&#58; 29&#8722;85&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">100 &#40;80&#46;6&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Negative&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">24 &#40;19&#46;4&#37;&#41;&nbsp;\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
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                  \t\t\t\t"><span class="elsevierStyleBold">PR</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Positive&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">86 &#40;69&#46;4&#37;&#41;&nbsp;\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
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                  \t\t\t\t">Negative&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Negative&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">87 &#40;70&#46;2&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleBold">Ki-67 Proliferative Index</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">&#60;14&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38 &#40;30&#46;6&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">&#8805;14&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">86 &#40;69&#46;4&#37;&#41;&nbsp;\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
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                  \t\t\t\t"><span class="elsevierStyleBold">Grade</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">I&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">14 &#40;11&#46;3&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">II&nbsp;\t\t\t\t\t\t\n
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                  \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 &#40;52&#46;4&#37;&#41;&nbsp;\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">III&nbsp;\t\t\t\t\t\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 &#40;36&#46;3&#37;&#41;&nbsp;\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="elsevierStyleBold">Molecular subtypes</span>&nbsp;\t\t\t\t\t\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">&nbsp;\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">Lum A&nbsp;\t\t\t\t\t\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 &#40;25&#37;&#41;&nbsp;\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">Lum B -&nbsp;\t\t\t\t\t\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 &#40;33&#46;9&#37;&#41;&nbsp;\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">Lum B &#43;&nbsp;\t\t\t\t\t\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 &#40;22&#46;6&#37;&#41;&nbsp;\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">HER-2 &#43;&nbsp;\t\t\t\t\t\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 &#40;7&#46;2&#37;&#41;&nbsp;\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">TN&nbsp;\t\t\t\t\t\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 &#40;11&#46;3&#37;&#41;&nbsp;\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="elsevierStyleBold">Axillary lymph node involvement</span>&nbsp;\t\t\t\t\t\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">&nbsp;\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">Positive&nbsp;\t\t\t\t\t\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">84 &#40;67&#46;7&#37;&#41;&nbsp;\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">Negative&nbsp;\t\t\t\t\t\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 &#40;32&#46;3&#37;&#41;&nbsp;\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="elsevierStyleBold">TNM stage</span>&nbsp;\t\t\t\t\t\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">&nbsp;\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">IA&nbsp;\t\t\t\t\t\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 &#40;5&#46;6&#37;&#41;&nbsp;\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">IB&nbsp;\t\t\t\t\t\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 &#40;22&#46;6&#37;&#41;&nbsp;\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">IIA&nbsp;\t\t\t\t\t\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 &#40;32&#46;3&#37;&#41;&nbsp;\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">IIB&nbsp;\t\t\t\t\t\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 &#40;13&#46;7&#37;&#41;&nbsp;\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">IIIA&nbsp;\t\t\t\t\t\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 &#40;14&#46;5&#37;&#41;&nbsp;\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">IIIB&nbsp;\t\t\t\t\t\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 &#40;11&#46;3&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2794663.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Patients&#8217; characteristics&#46;</p>"
        ]
      ]
      3 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0040"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:3 [
          "leyenda" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">AUC&#44; the area under the curve&#59; OR&#44; odds ratio&#59; CI&#59; confidence interval&#44; GLCM&#44; gray-level co-occurrence matrix&#59; GLZLM&#44; gray-level zone length matrix&#59; GLNU&#44; gray level non-uniformity&#59; SZLGE&#44; short-zone low gray-level emphasis&#59; SUVmax&#44; maximum standardized uptake value&#59; ER&#44; estrogen receptor&#59; PR&#44; progesterone receptor&#59; HER-2&#44; human epidermal growth factor receptor-2&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\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">Predictive Parameter&nbsp;\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&nbsp;\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">OR&nbsp;\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">95&#37; CI&nbsp;\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">p-value&nbsp;\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">ER-negativity&nbsp;\t\t\t\t\t\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">GLCM-Contrast&nbsp;\t\t\t\t\t\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&#46;755&nbsp;\t\t\t\t\t\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&#46;726&nbsp;\t\t\t\t\t\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&#46;561 &#8211; 0&#46;939&nbsp;\t\t\t\t\t\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&#46;015&nbsp;\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">PR-negativity&nbsp;\t\t\t\t\t\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">GLZLM-GLNU&nbsp;\t\t\t\t\t\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&#46;706&nbsp;\t\t\t\t\t\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&#46;9&nbsp;\t\t\t\t\t\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&#46;85 &#8211; 0&#46;95&nbsp;\t\t\t\t\t\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">&#60;0&#46;001&nbsp;\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">HER-2-positivity&nbsp;\t\t\t\t\t\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">Histogram-uniformity&nbsp;\t\t\t\t\t\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&#46;659&nbsp;\t\t\t\t\t\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&#46;028&nbsp;\t\t\t\t\t\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&#46;001 &#8211; 0&#46;683&nbsp;\t\t\t\t\t\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&#46;02&nbsp;\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">Triple-negativity&nbsp;\t\t\t\t\t\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">SUVmax&nbsp;\t\t\t\t\t\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&#46;776&nbsp;\t\t\t\t\t\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&#46;13&nbsp;\t\t\t\t\t\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&#46;01 &#8211; 1&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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