array:23 [ "pii" => "S2253808919300400" "issn" => "22538089" "doi" => "10.1016/j.remnie.2019.03.002" "estado" => "S300" "fechaPublicacion" => "2019-09-01" "aid" => "1064" "copyrightAnyo" => "2019" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2019;38:290-7" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 3 "formatos" => array:2 [ "HTML" => 1 "PDF" => 2 ] ] "Traduccion" => array:1 [ "es" => array:19 [ "pii" => "S2253654X18303032" "issn" => "2253654X" "doi" => "10.1016/j.remn.2019.02.004" "estado" => "S300" "fechaPublicacion" => "2019-09-01" "aid" => "1064" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2019;38:290-7" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 106 "formatos" => array:2 [ "HTML" => 70 "PDF" => 36 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Medidas de heterogeneidad global y esfericidad con <span class="elsevierStyleSup">18</span>F-FDG PET/TC en el cáncer de mama: relación con la biología tumoral, valor predictivo y pronóstico" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "290" "paginaFinal" => "297" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Global heterogeneity assessed with <span class="elsevierStyleSup">18</span>F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0030" "etiqueta" => "Fig. 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 2413 "Ancho" => 2492 "Tamanyo" => 214607 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Cortes coronales (A) y axiales (B) de la PET/TC con <span class="elsevierStyleSup">18</span>F-FDG que muestran una lesión hipermetabólica con distribución heterogénea del radiotrazador en la mama izquierda. La imagen segmentada (C), la distribución espacial de la escala de grises en los vóxeles (D) y el volumen volumétrico (E) mostraron los siguientes resultados: SUVmáx 13,36, SUVmedio 7,70, VTM 94,57<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, GTL 728,59<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, COV 0,20, índice SUVmedio/SUVmáx 0,58 y esfericidad 0,58. Tumor triple negativo (cT3N0M0), Ki-67: 20%. Tras recibir QN, hubo una respuesta completa. La SG y la SLE fueron ambas de 83 meses. La paciente está viva y libre de enfermedad.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "M.J. Tello Galán, A.M. García Vicente, J. Pérez Beteta, M. Amo Salas, G.A. Jiménez Londoño, F.J. Pena Pardo, Á.M. Soriano Castrejón, V.M. Pérez García" "autores" => array:8 [ 0 => array:2 [ "nombre" => "M.J." "apellidos" => "Tello Galán" ] 1 => array:2 [ "nombre" => "A.M." "apellidos" => "García Vicente" ] 2 => array:2 [ "nombre" => "J." "apellidos" => "Pérez Beteta" ] 3 => array:2 [ "nombre" => "M." "apellidos" => "Amo Salas" ] 4 => array:2 [ "nombre" => "G.A." "apellidos" => "Jiménez Londoño" ] 5 => array:2 [ "nombre" => "F.J." "apellidos" => "Pena Pardo" ] 6 => array:2 [ "nombre" => "Á.M." "apellidos" => "Soriano Castrejón" ] 7 => array:2 [ "nombre" => "V.M." "apellidos" => "Pérez García" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2253808919300400" "doi" => "10.1016/j.remnie.2019.03.002" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808919300400?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X18303032?idApp=UINPBA00004N" "url" => "/2253654X/0000003800000005/v1_201909070732/S2253654X18303032/v1_201909070732/es/main.assets" ] ] "itemSiguiente" => array:19 [ "pii" => "S2253808919300692" "issn" => "22538089" "doi" => "10.1016/j.remnie.2019.04.006" "estado" => "S300" "fechaPublicacion" => "2019-09-01" "aid" => "1075" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2019;38:298-304" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 7 "formatos" => array:2 [ "HTML" => 1 "PDF" => 6 ] ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Four years of clinical experience with Radium-223 for the treatment of castration-resistant prostate cancer" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "298" "paginaFinal" => "304" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Cuatro años de experiencia clínica con Radio-223 para el tratamiento del cáncer de próstata resistente a la castración" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 3773 "Ancho" => 2113 "Tamanyo" => 184668 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier survival curves, comparing patients according to number of administered injections.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "R.M. Álvarez Pérez, A. Delgado García, S. García Martínez, S. Sanz Viedma, H. Palacios Gerona, M. Pajares Vinardel, J. M. Jiménez-Hoyuela García" "autores" => array:7 [ 0 => array:2 [ "nombre" => "R.M." "apellidos" => "Álvarez Pérez" ] 1 => array:2 [ "nombre" => "A. Delgado" "apellidos" => "García" ] 2 => array:2 [ "nombre" => "S. García" "apellidos" => "Martínez" ] 3 => array:2 [ "nombre" => "S. Sanz" "apellidos" => "Viedma" ] 4 => array:2 [ "nombre" => "H. Palacios" "apellidos" => "Gerona" ] 5 => array:2 [ "nombre" => "M. Pajares" "apellidos" => "Vinardel" ] 6 => array:2 [ "nombre" => "J. M. Jiménez-Hoyuela" "apellidos" => "García" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X19300575" "doi" => "10.1016/j.remn.2019.04.004" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X19300575?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808919300692?idApp=UINPBA00004N" "url" => "/22538089/0000003800000005/v1_201909030619/S2253808919300692/v1_201909030619/en/main.assets" ] "itemAnterior" => array:19 [ "pii" => "S2253808919300813" "issn" => "22538089" "doi" => "10.1016/j.remnie.2019.06.001" "estado" => "S300" "fechaPublicacion" => "2019-09-01" "aid" => "1067" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2019;38:280-9" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 13 "formatos" => array:2 [ "HTML" => 9 "PDF" => 4 ] ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Comparative study of <span class="elsevierStyleSup">18</span>F-FDG PET/CT and CT angiography in the detection of large vessel vasculitis" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "280" "paginaFinal" => "289" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Estudio comparativo de la PET/TC con <span class="elsevierStyleSup">18</span>F-FDG y la angiografía por TC en la detección de la vasculitis de grandes vasos" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1745 "Ancho" => 1750 "Tamanyo" => 173369 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleSup">18</span>F-FDG PET/CT with <span class="elsevierStyleSup">18</span>F-FDG uptake predominantly in the left facial arteries. Coronal and axial PET (A and C) and coronal and axial PET/CT (B and D).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "M. Moragas Solanes, M. Andreu Magarolas, J.C. Martín Miramon, A.P. Caresia Aróztegui, M. Monteagudo Jiménez, J.C. Oliva Morera, C. Diaz Martín, A. Rodríguez Revuelto, Z. Bravo Ferrer, Ll. Bernà Roqueta" "autores" => array:10 [ 0 => array:2 [ "nombre" => "M." "apellidos" => "Moragas Solanes" ] 1 => array:2 [ "nombre" => "M." "apellidos" => "Andreu Magarolas" ] 2 => array:2 [ "nombre" => "J.C." "apellidos" => "Martín Miramon" ] 3 => array:2 [ "nombre" => "A.P." "apellidos" => "Caresia Aróztegui" ] 4 => array:2 [ "nombre" => "M." "apellidos" => "Monteagudo Jiménez" ] 5 => array:2 [ "nombre" => "J.C." "apellidos" => "Oliva Morera" ] 6 => array:2 [ "nombre" => "C." "apellidos" => "Diaz Martín" ] 7 => array:2 [ "nombre" => "A." "apellidos" => "Rodríguez Revuelto" ] 8 => array:2 [ "nombre" => "Z." "apellidos" => "Bravo Ferrer" ] 9 => array:2 [ "nombre" => "Ll." "apellidos" => "Bernà Roqueta" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X1930006X" "doi" => "10.1016/j.remn.2019.03.002" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X1930006X?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808919300813?idApp=UINPBA00004N" "url" => "/22538089/0000003800000005/v1_201909030619/S2253808919300813/v1_201909030619/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Global heterogeneity assessed with <span class="elsevierStyleSup">18</span>F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "290" "paginaFinal" => "297" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "María Jesús Tello Galán, Ana María García Vicente, Julián Pérez Beteta, Mariano Amo Salas, Germán Andrés Jiménez Londoño, Francisco José Pena Pardo, Ángel María Soriano Castrejón, Víctor Manuel Pérez García" "autores" => array:8 [ 0 => array:4 [ "nombre" => "María Jesús" "apellidos" => "Tello Galán" "email" => array:1 [ 0 => "mariajesustello1@gmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Ana María" "apellidos" => "García Vicente" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "Julián" "apellidos" => "Pérez Beteta" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 3 => array:3 [ "nombre" => "Mariano" "apellidos" => "Amo Salas" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] 4 => array:3 [ "nombre" => "Germán Andrés" "apellidos" => "Jiménez Londoño" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 5 => array:3 [ "nombre" => "Francisco José" "apellidos" => "Pena Pardo" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 6 => array:3 [ "nombre" => "Ángel María" "apellidos" => "Soriano Castrejón" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 7 => array:3 [ "nombre" => "Víctor Manuel" "apellidos" => "Pérez García" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Mathematical Oncology Laboratory (MôLAB), Castilla La Mancha University, Ciudad Real, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Department of Mathematics, Castilla La Mancha University. Ciudad Real, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Medidas de heterogeneidad global y esfericidad con <span class="elsevierStyleSup">18</span>F-FDG PET/TC en el cáncer de mama: relación con biología tumoral, valor predictivo y pronóstico" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1188 "Ancho" => 2344 "Tamanyo" => 131679 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Relationship between the sphericity, SUVmean/SUVmax ratio and DFS.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Breast cancer is the neoplasm with the highest incidence and prevalence in women, and thus, the subject of numerous studies. The use of <span class="elsevierStyleSup">18</span>F-fluorodeoxyglucose positron emission tomography/computed tomography (<span class="elsevierStyleSup">18</span>F-FDG PET/CT) provides a significant improvement in diagnostic accuracy and has a considerable impact on locally advanced breast cancer (LABC) management, including initial staging, optimization of treatment, restaging and monitoring of the response to therapy.<a class="elsevierStyleCrossRefs" href="#bib0200"><span class="elsevierStyleSup">1–5</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">An important feature of malignant tumors is the heterogeneity of constituent cells. Heterogeneity appears as alterations in phenotypic features and as variations in behavioral characteristics,<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">6,7</span></a> and can be assessed on imaging techniques such as ultrasound, CT and PET/CT through different quantitative methods such as textural analysis.<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">8–11</span></a> These variables have been related to biological tumor features and provide additional predictive and prognostic information, which leads to the concept of radiomics.<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">12–15</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">However, the computational complexity in the obtention of textural variables of second and third order, and also their lack of robustness, justify exploring the potential clinical value of other measures of “more global” spatial heterogeneity, such as the coefficient of variation (COV) and sphericity.</p><p id="par0020" class="elsevierStylePara elsevierViewall">COV analyzes the spatial dispersion of gray intensity levels in voxels, without considering the relationships between them.<a class="elsevierStyleCrossRefs" href="#bib0275"><span class="elsevierStyleSup">16,17</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Sphericity quantifies the irregularity of the FDG uptake in the contour of a tumor in the three dimensions.<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">18–21</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Nevertheless, although several works support a predictive aim of these more simplified heterogeneity variables in the prediction of tumor biology, response and prognosis in other tumors, their application in breast cancer has been scarcely reported.<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">11,17–19,22–25</span></a></p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Patients</span><p id="par0035" class="elsevierStylePara elsevierViewall">All reported patients were participants of an ongoing prospective study started in September 2009. The Institutional Review Board approved this paper, and written informed consent was obtained from all patients.</p><p id="par0040" class="elsevierStylePara elsevierViewall">The inclusion criteria were the following: (1) newly diagnosed breast cancer with clinical indication of neoadjuvant chemotherapy (NC), (2) lesion uptake higher than background, (3) absence of distant metastases confirmed by other imaging techniques previous to the request of the PET/CT for staging, and (4) breast lesion size of at least 2<span class="elsevierStyleHsp" style=""></span>cm.</p><p id="par0045" class="elsevierStylePara elsevierViewall">In addition, the immunohistochemical profile, including estrogen receptors (ER) and progesterone receptors (PR), expression of the HER-2 oncogene, Ki-67 proliferation index and tumor histological grade, was obtained.</p><p id="par0050" class="elsevierStylePara elsevierViewall">Patients were classified into 3 groups: low risk (luminal A), intermediate risk (luminal B Her-2 negative/Her-2 positive) and high risk [human epidermal growth factor receptor 2 (Her-2 enriched) and triple negative], according to the molecular phenotype of the lesions.</p><p id="par0055" class="elsevierStylePara elsevierViewall">All patients received a standard approved neoadjuvant regimen, which consisted of a combination of an anthracyclines and taxanes. There were two regimen options, which were selected by the investigator in each center:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">–</span><p id="par0060" class="elsevierStylePara elsevierViewall">DDC scheme: docetaxel, doxorubicin and cyclophosphamide every 3 weeks, for six cycles.</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">–</span><p id="par0065" class="elsevierStylePara elsevierViewall">Sequential regimen: doxorubicin/cyclophosphamide three-weekly, for four cycles, followed by docetaxel (with or without trastuzumab if HER2-possitive) every 21 days for four cycles.</p></li></ul></p><p id="par0070" class="elsevierStylePara elsevierViewall">Response to NC, overall survival (OS) and disease-free survival (DFS) were determined following the specifications of a previous publication.<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">26</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">FDG PET/CT acquisition</span><p id="par0075" class="elsevierStylePara elsevierViewall">All PET/CT examinations were performed on the same dedicated whole-body PET/CT device (Discovery DSTE-16s, GE Medical Systems) in three-dimensional (3D) mode. The acquisition began 60<span class="elsevierStyleHsp" style=""></span>minutes after intravenous administration of approximately 370 MBq (10<span class="elsevierStyleHsp" style=""></span>mCi) of <span class="elsevierStyleSup">18</span>F-FDG, and was performed following a standardized protocol.<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">27</span></a> It was necessary to carry out the study in the supine position, since we did not have the instrumentation required for the prone position. The image voxel size was 5.47<span class="elsevierStyleHsp" style=""></span>mm<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>5.47<span class="elsevierStyleHsp" style=""></span>mm<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>3.27<span class="elsevierStyleHsp" style=""></span>mm, with a slice thickness of 3.27<span class="elsevierStyleHsp" style=""></span>mm and no gap between slices. Matrix size was 128<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>128.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Image analysis</span><p id="par0080" class="elsevierStylePara elsevierViewall">PET images in DICOM (Digital Imaging and Communication in Medicine) files were imported into the scientific software package Matlab (R2015b, The MathWorks, Inc., Natick, MA, USA) and pre-processed using in-house semiautomatic image segmentation software. The primary tumor was first manually located in a 3D box, and then automatically segmented in three dimensions.</p><p id="par0085" class="elsevierStylePara elsevierViewall">The regions in the 3D box equal to or above 40% the SUVmax were selected to automatically delineate the volume of interest (VOI). In case of central hypometabolism and a metabolic activity below the selected threshold value, this volume was considered as necrosis and excluded from the volume assessment. In case of multiple breast lesions (multicenter or multifocal cancer), those with the highest FDG uptake were selected for the analysis.</p><p id="par0090" class="elsevierStylePara elsevierViewall">Then, metabolic parameters were obtained [SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)], as well as global heterogeneity measures (COV and SUVmean/SUVmax ratio), and lastly, the sphericity.</p><p id="par0095" class="elsevierStylePara elsevierViewall">SUVmax is defined as the maximum uptake value in the segmented tumor, which reflects maximum tissue concentration of FDG in the volume of interest (VOI). SUVmean reflects the average uptake value in the VOI. MTV is the volume of the VOI after segmentation. TLG is the product of SUVmean by MTV.</p><p id="par0100" class="elsevierStylePara elsevierViewall">The formula used for the SUV computations was as follows:<elsevierMultimedia ident="eq0005"></elsevierMultimedia>SV is the stored value, RS the rescaled slope, <span class="elsevierStyleItalic">W</span> is the patient weight, RTD is the radiopharmaceutical injected dose and HF its half-life, DF is the decay factor, and Et is the elapsed time for each slice processed.</p><p id="par0105" class="elsevierStylePara elsevierViewall">COV analyzes the spatial dispersion of gray intensity levels in voxels, without considering the relationships between them.<a class="elsevierStyleCrossRefs" href="#bib0275"><span class="elsevierStyleSup">16,17</span></a> It is a statistical measure of the dispersion of data points in a data series around the mean and can be calculated as follows: standard deviation SUV/SUVmean.<a class="elsevierStyleCrossRefs" href="#bib0280"><span class="elsevierStyleSup">17,25,28</span></a></p><p id="par0110" class="elsevierStylePara elsevierViewall">Tumors with COV values below 0.30 were defined as homogeneous, and those with COV<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>0.30 were classified as heterogeneous.<a class="elsevierStyleCrossRef" href="#bib0335"><span class="elsevierStyleSup">28</span></a></p><p id="par0115" class="elsevierStylePara elsevierViewall">The SUVmean/SUVmax ratio was also used as a measure of gray-level dispersion (global heterogeneity). Lesions with SUVmax similar to SUVmean have values closer to 1.</p><p id="par0120" class="elsevierStylePara elsevierViewall">As previously said, sphericity quantifies the irregularity of the FDG uptake in the contour of a tumor in the three dimensions.<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">18–21</span></a> For its calculation, we used the following formula.<a class="elsevierStyleCrossRef" href="#bib0340"><span class="elsevierStyleSup">29</span></a><elsevierMultimedia ident="eq0010"></elsevierMultimedia>Total volume (in cubic centimeters), total surface (in square centimeters), and <span class="elsevierStyleItalic">S</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">R</span></span> is a dimensionless ratio between the segmented tumor volumen and the volumen that a spherical tumor with the same surface would have.</p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Statistical analysis</span><p id="par0125" class="elsevierStylePara elsevierViewall">Statistical analysis was performed using SPSS software (v. 22.0.00 IBM, New York, NY, USA). Qualitative variables were summarized using percentages and frequencies, and quantitative variables using mean and standard deviation (SD).</p><p id="par0130" class="elsevierStylePara elsevierViewall">Kolmogorov–Smirnov analysis was performed to compare global heterogeneity measures and sphericity.</p><p id="par0135" class="elsevierStylePara elsevierViewall">Spearman's rank correlation coefficient was calculated for comparing global heterogeneity measures and sphericity with SUVs and volumetric measures.</p><p id="par0140" class="elsevierStylePara elsevierViewall">To study the relationship between global heterogeneity variables with the response to NC, the Student's T test was used.</p><p id="par0145" class="elsevierStylePara elsevierViewall">Finally, Cox regression analysis was performed to study the relationship between global heterogeneity variables and sphericity with global survival and disease-free survival.</p><p id="par0150" class="elsevierStylePara elsevierViewall">The following values were considered relevant and statistically significant respectively: <span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>0.5 and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Results</span><p id="par0155" class="elsevierStylePara elsevierViewall">Of the 68 patients included, the majority (60.3%) corresponded to the intermediate risk group. 62 patients received NC, but only 18 responded.</p><p id="par0160" class="elsevierStylePara elsevierViewall">During the follow-up, 13 patients relapsed and 11 of them died. <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows patients’ characteristics.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0165" class="elsevierStylePara elsevierViewall">The mean values<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD of COV, SUV mean/SUVmax and sphericity were 0.25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03, 0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.04 and 0.774<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.15, respectively. Thus, in general, breast lesions were homogeneous and spherical.</p><p id="par0170" class="elsevierStylePara elsevierViewall">Regarding the associations between the metabolic variables, a negative and significant association was found between MTV and COV (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.768; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.000), MTV and sphericity (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.548; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.000) and COV with TLG (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.656; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.000). <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> shows the relations graphically.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0175" class="elsevierStylePara elsevierViewall">The results of the associations between the global heterogeneity variables (COV, SUVmean/SUVmax and sphericity), with SUV (SUVmax and SUVmean) and volumetric variables (MTV and TLG) are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> and <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>. No significant associations were found between COV and SUVmean/SUVmax (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.029, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.818) and between COV and sphericity (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.229, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.065). However, a significant association was found between sphericity and SUVmean/SUVmax (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.581, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.000).</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0180" class="elsevierStylePara elsevierViewall">The only parameter that showed a significant association with the classification in risk categories was the COV (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.027). Tumors with a lower COV did not express ER (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.018) and had a high Ki-67 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001), and also those belonging to the high-risk group (Her-2 enriched and triple negative) when were compared to the low risk group (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). Neither the SUVmean/SUVmax ratio nor the sphericity had significant associations with the biological characteristics (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.805 and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.304 respectively).</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0185" class="elsevierStylePara elsevierViewall">No PET variable (COV, SUVmean/SUVmax and sphericity) showed an association with the response to NC (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.300, 0.097 and 0.851 respectively) or the OS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.111, 0.238 and 0.487 respectively).</p><p id="par0190" class="elsevierStylePara elsevierViewall">The sphericity, the SUVmean/SUVmax index and the COV were inversely related to the DFS (<span class="elsevierStyleItalic">B</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.464, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.041; <span class="elsevierStyleItalic">B</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−2.439, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.055; <span class="elsevierStyleItalic">B</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−1.736, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.263 in each case, being <span class="elsevierStyleItalic">B</span> the value of the adjusted parameter of the Cox proportional hazard regression model). However, the sphericity was the only statistically significant variable, so that, for every tenth that sphericity increases, the risk of recurrence decreases by 37% as shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0195" class="elsevierStylePara elsevierViewall">Some representative cases are presented in <a class="elsevierStyleCrossRefs" href="#fig0020">Figs. 4–6</a>.</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><elsevierMultimedia ident="fig0025"></elsevierMultimedia><elsevierMultimedia ident="fig0030"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Discussion</span><p id="par0200" class="elsevierStylePara elsevierViewall">Heterogeneity in breast cancer obtained from <span class="elsevierStyleSup">18</span>F-FDG PET/CT has been quantified in several previous studies using textural variables of second and third order. Those measures study spatial relationships between gray levels in different voxels. However, the complexity of their calculation and the lack of robustness in the results, limit their clinical applicability.<a class="elsevierStyleCrossRefs" href="#bib0320"><span class="elsevierStyleSup">25,30–32</span></a></p><p id="par0205" class="elsevierStylePara elsevierViewall">For this reason, global heterogeneity variables, much easier to compute, are being explored. One of them is the COV, which represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from one another. The COV shows an inverse relationship with homogeneity, so the lesions are more homogeneous as their COV is lower.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">17</span></a></p><p id="par0210" class="elsevierStylePara elsevierViewall">The threshold COV values differentiating homogeneous from heterogeneous lesions depend on the study but most of them are around 0.3, the chosen value in the present study.<a class="elsevierStyleCrossRefs" href="#bib0280"><span class="elsevierStyleSup">17,28</span></a></p><p id="par0215" class="elsevierStylePara elsevierViewall">Also, since SUVmean offers more integrated information of the tumor voxels than the SUVmax, we introduced a novel variable, the SUVmean/SUVmax ratio. Contrary to the COV, the SUV ratio shows a direct relationship with homogeneity. So, when the ratio increases, the homogeneity is larger.</p><p id="par0220" class="elsevierStylePara elsevierViewall">Furthermore, some authors have introduced the parameter of tumor sphericity, defined as how close the tumor is to a sphere with a similar volume.<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">25</span></a></p><p id="par0225" class="elsevierStylePara elsevierViewall">Tumor heterogeneity is classically associated with cellular proliferation, necrosis, hypoxia and angiogenesis, factors related to higher tumor aggressiveness and poorer prognosis in many cancers.<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">33</span></a></p><p id="par0230" class="elsevierStylePara elsevierViewall">It is known that in textural analysis of <span class="elsevierStyleSup">18</span>F-FDG-PET/CT breast cancer, images yield informative data on to metabolic heterogeneity, and thus, on the disease biological behavior.<a class="elsevierStyleCrossRefs" href="#bib0365"><span class="elsevierStyleSup">34–36</span></a> Previous works have shown that tumor heterogeneity is higher in breast cancer with poor prognosis pathological factors; thus, it might be used as a non-invasive tool to assess breast cancer aggressiveness. Nevertheless, the experience on global heterogeneity variables is very limited, and we wanted to study those measures in LABC.<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">24</span></a></p><p id="par0235" class="elsevierStylePara elsevierViewall">In our dataset, most of the included lesions were homogeneous (low COV and high SUVmean/SUVmax values) and spherical. The high sphericity value found (0.774<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.15) could be in part due to the low volume of the lesions, what may lead to round-shaped tumors because of their limited spatial resolution.</p><p id="par0240" class="elsevierStylePara elsevierViewall">We do not found relation between the SUV variables and sphericity (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). A possible explanation is that sphericity seems to refer to the infiltrative tumor capacity of the tumor and the SUV is more related to its proliferative potential. Similar results have been described for other tumors.<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">18,20</span></a></p><p id="par0245" class="elsevierStylePara elsevierViewall">Our lesions showed an inverse relation between MTV and TLG with sphericity, that means that bigger lesions and with the highest metabolic burden were less spherical, that probably is related with larger tumors correspond to more advanced stages, where they become more infiltrative, less homogenous, and thus, more aggressive.</p><p id="par0250" class="elsevierStylePara elsevierViewall">Nonetheless, the inverse relation found between volume variables and COV offered us a contradictory result (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>), so that, at a higher volume or glycolysis rate, there is less dispersion between the gray intensity of the voxels, and therefore, the lesions are more homogeneous.</p><p id="par0255" class="elsevierStylePara elsevierViewall">These results had already been previously obtained for other cancer types.<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">18,20</span></a> In fact, the factors that mediate a correlation between sphericity and MTV (and therefore with the TLG due to the collinearity between the MTV and the TLG) include spatial resolution and necrosis. Based on the limited spatial resolution of PET imaging, small lesions appear more spherical than they actually might be. On the other hand, necrosis, which results in a decreased sphericity by producing additional internal surface of MTV, is more likely to occur in large tumors than in small ones.</p><p id="par0260" class="elsevierStylePara elsevierViewall">In relation to sphericity, we found a direct relationship with the SUVmean/SUVmax ratio, so that, the most spherical lesions had a higher ratio and were more homogeneous. Since the SUVmean/SUVmax ratio was first used in this study, there are no previous reported experience regarding the relationship between sphericity and this parameter.</p><p id="par0265" class="elsevierStylePara elsevierViewall">Some studies have demonstrated that PET parameters measured before NC have prognostic value in patients with LABC. The results obtained suggest that <span class="elsevierStyleSup">18</span>F-FDG-PET image-derived indices, notably volume parameters, may be helpful to plan patient follow-up, as well as to select high-risk patients within trials investigating novel treatment strategies.<a class="elsevierStyleCrossRefs" href="#bib0380"><span class="elsevierStyleSup">37–39</span></a></p><p id="par0270" class="elsevierStylePara elsevierViewall">Regarding global heterogeneity variables, several authors consider that sphericity provides better prognostic value for DFS and OS compared to SUV, MTV and TLG.<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">11,18,21–24</span></a> In addition, raised COV as an indicator of tumor heterogeneity, has been described as a predictive factor of a worse treatment outcome, being the thresholds 0.37 and 0.47, respectively.<a class="elsevierStyleCrossRefs" href="#bib0275"><span class="elsevierStyleSup">16,17</span></a></p><p id="par0275" class="elsevierStylePara elsevierViewall">In the present study, after applying the Cox regression analysis, we observed that no PET variable (COV, SUVmean/SUVmax and sphericity) showed association with the response to the NC nor OS. However, in the case of DFS, the sphericity was significantly associated (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.041) and the SUVmean/SUVmax ratio was very close to the significance (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.055). So that, for every tenth that sphericity increased, the risk of recurrence decreased by 37% (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>). Previous studies support these findings, since the sphericity of pretherapeutic FDG uptake seems to provide better prognostic value for DFS compared to SUV and volumetric variables.<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">18,24</span></a></p><p id="par0280" class="elsevierStylePara elsevierViewall">The main limitation of the present work was the limited spatial resolution of PET images and the reduced volume of some included lesions, which makes them appear as round because they have very few voxels. The reduced sample of patients especially in the case of NC response evaluation could affect our results.</p><p id="par0285" class="elsevierStylePara elsevierViewall">Moreover, nowadays, there is no established a threshold value for the interpretation of global heterogeneity measures (COV and SUVmean/SUVmax ratio), thus, additional studies are necessary.</p><p id="par0290" class="elsevierStylePara elsevierViewall">However, although global heterogeneity variables deserve more investigation in order to establish their predictive and prognostic aim, this work offers a novel approximation regarding their impact in patients with LABC.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conclusions</span><p id="par0295" class="elsevierStylePara elsevierViewall">In our dataset, breast tumors behaved as homogeneous and spherical lesions. A larger tumor volume was associated with a lower sphericity.</p><p id="par0300" class="elsevierStylePara elsevierViewall">Global heterogeneity variables, as COV, SUVmean/SUVmax and sphericity, did not predict the response to NC and were not associated with OS. Sphericity was the only variable that showed a statistically significant relationship with DFS, that is, the most spherical tumors showed a lower risk of recurrence.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conflict of interest</span><p id="par0305" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres1238160" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Aim" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Material and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1149272" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1238159" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1149271" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Patients" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "FDG PET/CT acquisition" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Image analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0030" "titulo" => "Statistical analysis" ] 7 => array:2 [ "identificador" => "sec0035" "titulo" => "Results" ] 8 => array:2 [ "identificador" => "sec0040" "titulo" => "Discussion" ] 9 => array:2 [ "identificador" => "sec0045" "titulo" => "Conclusions" ] 10 => array:2 [ "identificador" => "sec0050" "titulo" => "Conflict of interest" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2018-11-29" "fechaAceptado" => "2019-02-26" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1149272" "palabras" => array:7 [ 0 => "<span class="elsevierStyleSup">18</span>F-FDG PET/CT" 1 => "Breast cancer" 2 => "Global heterogeneity variables" 3 => "Sphericity" 4 => "Coefficient of variation" 5 => "Response" 6 => "Prognosis" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1149271" "palabras" => array:7 [ 0 => "<span class="elsevierStyleSup">18</span>F-FDG PET/TC" 1 => "Cáncer de mama" 2 => "Medidas de heterogeneidad global" 3 => "Esfericidad" 4 => "Coeficiente de variación" 5 => "Respuesta" 6 => "Pronóstico" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Aim</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To analyze the relationship between measurements of global heterogeneity, obtained from <span class="elsevierStyleSup">18</span>F-FDG PET/CT, with biological variables and their predictive and prognostic role in patients with locally advanced breast cancer (LABC).</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Material and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">68 patients from a multicenter and prospective study, with LABC and a baseline <span class="elsevierStyleSup">18</span>F-FDG PET/CT were included. Immunohistochemical profile [estrogen receptors (ER) and progesterone receptors (PR), expression of the HER-2 oncogene, Ki-67 proliferation index and tumor histological grade], response to neoadjuvant chemotherapy (NC), overall survival (OS) and disease-free survival (DFS) were obtained as clinical variables. Three-dimensional segmentation of the lesions, providing SUV, volumetric [metabolic tumor volume (MTV) and total lesion glycolysis (TLG)] and global heterogeneity variables [coefficient of variation (COV) and SUVmean/SUVmax ratio], as well as sphericity was performed. The correlation between the results obtained with the immunohistochemical profile, the response to NC and survival was also analyzed.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Of the patients included, 62 received NC. Only 18 responded. 13 patients relapsed and 11 died during follow-up. ER negative tumors had a lower COV (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.018) as well as those with high Ki-67 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001) and high risk phenotype (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.033) compared to the rest. No PET variable showed association with the response to NC nor OS. There was an inverse relationship between sphericity with DFS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.041), so, for every tenth that sphericity increases, the risk of recurrence decreases by 37%.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Breast tumors in our LABC dataset behaved as homogeneous and spherical lesions. Larger volumes were associated with a lower sphericity. Global heterogeneity variables and sphericity do not seem to have a predictive role in response to NC nor in OS. More spherical tumors with less variation in gray intensity between voxels showed a lower risk of recurrence.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Aim" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Material and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Determinar la relación de las medidas de heterogeneidad global y la esfericidad tumoral obtenidas en 18F-FDG PET/TC con variables biológicas, así como su papel predictivo y pronóstico en pacientes con cáncer de mama localmente avanzado (CMLA).</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Material y métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se incluyeron 68 pacientes con CMLA, con indicación de tratamiento neoadyuvante (TNA) y18F-FDG PET/TC basal procedentes de un estudio prospectivo multicéntrico en curso. Se determinó el perfil inmunohistoquímico [receptores de estrógenos (RE) y de progesterona (RP), expresión del oncogén HER-2, índice de proliferación Ki-67 y grado histológico tumoral], la respuesta al TNA, la supervivencia global (SG) y la supervivencia libre de enfermedad (SLE). Se realizó la segmentación tridimensional de las lesiones, obteniendo variables SUV, volumétricas y de heterogeneidad global, así como la esfericidad. También se analizó la correlación entre los resultados obtenidos con el perfil inmunohistoquímico, la respuesta a la TNA y la supervivencia.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">De las pacientes incluidas, 62 recibieron TNA, respondiendo a éste sólo 18. 13 pacientes recidivaron y 11 fallecieron durante el seguimiento. Los tumores que no expresaron RE tuvieron un COV inferior (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.018), así como los de Ki-67 alto (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001) y los de fenotipo de alto riesgo (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.033) frente al resto. Ninguna variable PET mostró asociación con la respuesta al TNA ni con la SG. La esfericidad y el índice SUVmedio/SUVmax se relacionaron con la SLE de forma inversa (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.041 y p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.055, respectivamente) de modo que, por cada décima que aumenta la esfericidad, el riesgo de recurrencia disminuye en un 37%.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Los tumores de mama localmente avanzados incluidos en nuestra muestra se comportaron como lesiones homogéneas y esféricas. Los de mayor volumen se asociaron con menor esfericidad. Las variables de heterogeneidad global y la esfericidad no parecen tener un papel predictivo en respuesta a la TNA ni en la SG. Los tumores más esféricos y con menos variación en la intensidad de gris entre los voxels mostraron un menor riesgo de recurrencia.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Please cite this article as: Tello Galán MJ, García Vicente AM, Julián PB, Mariano AS, Jiménez Londoño GA, Pena Pardo FJ, et al. Medidas de heterogeneidad global y esfericidad con <span class="elsevierStyleSup">18</span>F-FDG PET/TC en el cáncer de mama: relación con biología tumoral, valor predictivo y pronóstico. Rev Esp Med Nucl Imagen Mol. 2019;38;290–297.</p>" ] ] "multimedia" => array:11 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 956 "Ancho" => 2356 "Tamanyo" => 107484 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Relationships between the COV and volumetric variables (MTV and TLG). MTV and the TLG showed an inverse relationship with the COV.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 981 "Ancho" => 2360 "Tamanyo" => 128101 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Association between sphericity, SUVmean/SUVmax and MTV. The sphericity showed a direct relationship with the SUVmean/SUVmax ratio, and an inverse one with the MTV.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1188 "Ancho" => 2344 "Tamanyo" => 131679 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Relationship between the sphericity, SUVmean/SUVmax ratio and DFS.</p>" ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 2703 "Ancho" => 2501 "Tamanyo" => 252986 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Coronal (A) and axial (B) slices of <span class="elsevierStyleSup">18</span>F-FDG PET/CT show hypermetabolic lesion, with heterogeneous distribution of the radiotracer in the left breast. Segmented image (C), spatial distribution of gray levels in voxels (D), and volumetric model (E) obtained the following results: SUVmax: 23.08, SUVmean: 13.37, MTV: 44.11<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, TLG: 589.93<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, COV: 0.22, SUVmean/SUVmax: 0.58 and sphericity: 0.56. Luminal B Her-2 negative tumor (cT3N0M0), Ki-67: 20%. No histological response to NC. The OS and the DFS were 95 months in both cases. Patient is alive and free of recurrence.</p>" ] ] 4 => array:7 [ "identificador" => "fig0025" "etiqueta" => "Fig. 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 2089 "Ancho" => 2520 "Tamanyo" => 182758 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Coronal (A) and axial (B) slices of <span class="elsevierStyleSup">18</span>F-FDG PET/CT show hypermetabolic lesion, with heterogeneous distribution of the radiotracer, located in the right breast. Segmented image (C), spatial distribution of gray levels in voxels (D), and volumetric model (E) obtained the following results: SUVmax 4.62, SUVmean 2.87, MTV 6.16<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, TLG 17.72<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, COV 0.30, SUVmean/SUVmax 0.62 and sphericity 0.95. Luminal B Her-2 positive tumor (cT1N1M0), Ki-67 not available. No histological response to NC. The OS and the DFS were 78 months in both cases. Patient is alive and free of recurrence.</p>" ] ] 5 => array:7 [ "identificador" => "fig0030" "etiqueta" => "Fig. 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 2413 "Ancho" => 2492 "Tamanyo" => 215329 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Coronal (A) and axial (B) slices of <span class="elsevierStyleSup">18</span>F-FDG PET/CT show hypermetabolic lesion, with heterogeneous distribution of the radiotracer in the left breast. Segmented image (C), spatial distribution of gray levels in voxels (D), and volumetric model (E) obtained the following results: SUVmax 13.36, SUVmean 7.70, MTV 94.57<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, TLG 728.59<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, COV 0.20, SUVmean/SUVmax 0.58 and sphericity 0.58. Triple negative tumor (cT3N0M0), Ki-67: 20%. After receiving NC, the response was complete. The OS and the DFS were 83 months in both cases. Patient is alive and free of recurrence.</p>" ] ] 6 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">n</span>: number of patients, NC: neoadjuvant chemotherapy; ER: estrogen receptor, PR: progesterone receptor.</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">Patients’ characteristics \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>68 (100%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">ER</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">44 (64.7%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (33.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Unknown \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">PR</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35 (51.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">32 (47.0%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Unknown \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">HER-2</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19 (27.9%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48 (70.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Unknown \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Ki 67</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>High (≥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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54 (79.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Low (<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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (8.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Unknown \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (11.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Phenotypes</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Luminal 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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (7.4%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Luminal B 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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 (38.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Luminal B 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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (22.0%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Her-2 enriched \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (5.9%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Triple 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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (26.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Risk categories</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Low risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (7.4%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Intermediate risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">41 (60.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>High risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22 (32.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Staging</span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>T</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>T1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (11.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>T2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52 (76.4%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>T3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (11.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>N</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>N0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35 (51.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>N1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 (10.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>N2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (16.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>N3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (22.1%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>M</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>M0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">68 (100%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Necrosis</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (14.7%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58 (85.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Focality</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Unifocal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50 (73.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Bifocal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (16.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Multifocal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 (10.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">NC</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62 (91.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (8.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Response to NC</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (29.0%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43 (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"><span class="elsevierStyleHsp" style=""></span>Unknown \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Recurrence</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (19.1%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55 (80.9%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Death</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (16.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57 (83.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2115635.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Patients’ characteristics.</p>" ] ] 7 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">COV: coefficient of variation, MTV: metabolic tumor volume; TLG: total lesion glycolysis.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" 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">SUVmax \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">SUVmean \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">MTV \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">TLG \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " rowspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">COV</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">r</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.304 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.306 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.768<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.656<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.013<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.012<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.000<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.000<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " rowspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SUVmean/SUVmax</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">r</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.220 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.301 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.074 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.110 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.077 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.014<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.554 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.379 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " rowspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sphericity</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">r</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.003 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.048 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.548<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.366 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.980 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.700 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.000<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.003<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2115633.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">The results with correlation coefficient >0.5 and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05 are shown with an asterisk.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Association between global heterogeneity variables (COV and SUV mean/SUVmax) and sphericity, with SUV and volumetric variables.</p>" ] ] 8 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">ER: estrogen receptor, PR: progesterone receptor, COV: coefficient of variation, SD: standard deviation.</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">Immunohistochemical profile \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">COV<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \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">SUVmean/SUVmax \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \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">Sphericity \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.018 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.906 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.77<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.018 \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">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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.24<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.71<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" 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">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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.556 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.749 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.78<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.071 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.24<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.04 \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.71<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" 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">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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.367 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.04 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.847 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.79<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.129 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.04 \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.73<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.14 \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="" 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">Ki-67<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>14% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.24<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.04 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.540 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.74<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.306 \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<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>14% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.28<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.62<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.80<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" 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">High risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.24<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.033 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.805 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.71<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.304 \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">Low risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.26<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.60<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.72<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2115634.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Relationship between immunohistochemical profile, COV, SUVmean/SUVmax and sphericity.</p>" ] ] 9 => array:5 [ "identificador" => "eq0005" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "SUV=SV⋅RS⋅WRTD⋅DF⋅e(−Ln(2)⋅Et/HF)" "Fichero" => "STRIPIN_si1.jpeg" "Tamanyo" => 2850 "Alto" => 40 "Ancho" => 213 ] ] 10 => array:5 [ "identificador" => "eq0010" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "SR=6πtotal   volume(total   surface)3" "Fichero" => "STRIPIN_si2.jpeg" "Tamanyo" => 3247 "Alto" => 50 "Ancho" => 198 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:39 [ 0 => array:3 [ "identificador" => "bib0200" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Early diagnosis of breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "L. Wang" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:4 [ "tituloSerie" => "Sensors (Basel)" "fecha" => "2017" "volumen" => "17" "paginaInicial" => "pii:E1572" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0205" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Diagnostic role of fluorodeoxyglucose PET in breast cancer: a history to current application" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "D. Chakraborty" 1 => "S. Basu" 2 => "G.A. Ulaner" 3 => "A. Alavi" 4 => "R. Kumar" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.cpet.2018.02.011" "Revista" => array:6 [ "tituloSerie" => "PET Clin" "fecha" => "2018" "volumen" => "13" "paginaInicial" => "355" "paginaFinal" => "361" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/30100075" "web" => "Medline" ] ] ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0210" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The evolving role of FDG-PET/CT in the diagnosis, staging, and treatment of breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "K. Paydary" 1 => "S.M. Seraj" 2 => "M.Z. Zadeh" 3 => "S. Emamzadehfard" 4 => "S.P. Shamchi" 5 => "S. Gholami" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:2 [ "tituloSerie" => "Mol Imaging Biol" "fecha" => "2018" ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0215" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18F-FDG PET/CT in breast cancer: evidence-based recommendations in initial staging" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A.P. Caresia Aroztegui" 1 => "A.M. García Vicente" 2 => "S. Álvarez Ruiz" 3 => "R.C. Delgado Bolton" 4 => "J. Orcajo Rincón" 5 => "J.R. García Garzón" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:3 [ "tituloSerie" => "Tumour Biol" "fecha" => "2017" "volumen" => "39" ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0220" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Present and future role of FDG-PET/CT imaging in the management of breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "K. Kitajima" 1 => "Y. Miyoshi" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s11604-015-0516-0" "Revista" => array:6 [ "tituloSerie" => "Jpn J Radiol" "fecha" => "2016" "volumen" => "34" "paginaInicial" => "167" "paginaFinal" => "180" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26733340" "web" => "Medline" ] ] ] ] ] ] ] ] 5 => array:3 [ "identificador" => "bib0225" "etiqueta" => "6" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Tumor heterogeneity: morphological, molecular and clinical implications" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "M.E. Lleonart" 1 => "P. Martín-Duque" 2 => "R. Sánchez-Prieto" 3 => "A. Moreno" 4 => "S. Ramón y Cajal" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.14670/HH-15.881" "Revista" => array:6 [ "tituloSerie" => "Histol Histopathol" "fecha" => "2000" "volumen" => "15" "paginaInicial" => "881" "paginaFinal" => "898" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/10963132" "web" => "Medline" ] ] ] ] ] ] ] ] 6 => array:3 [ "identificador" => "bib0230" "etiqueta" => "7" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Prognostic implication of intratumoral metabolic heterogeneity in invasive ductal carcinoma of the breast" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S.H. Son" 1 => "D.H. Kim" 2 => "C.M. Hong" 3 => "C.Y. Kim" 4 => "S.Y. Jeong" 5 => "S.W. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1186/1471-2407-14-585" "Revista" => array:5 [ "tituloSerie" => "BMC Cancer" "fecha" => "2014" "volumen" => "14" "paginaInicial" => "585" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25112709" "web" => "Medline" ] ] ] ] ] ] ] ] 7 => array:3 [ "identificador" => "bib0235" "etiqueta" => "8" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "3D breast ultrasound: a significant predictor in breast cancer reduction under pre-operative chemotherapy" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "M. Warm" 1 => "V. Duda" 2 => "C. Eichler" 3 => "N. Harbeck" 4 => "A. Gossmann" 5 => "A. Thomas" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Anticancer Res" "fecha" => "2011" "volumen" => "31" "paginaInicial" => "4039" "paginaFinal" => "4042" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/22110239" "web" => "Medline" ] ] ] ] ] ] ] ] 8 => array:3 [ "identificador" => "bib0240" "etiqueta" => "9" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "T. Peikert" 1 => "F. Duan" 2 => "S. Rajagopalan" 3 => "R.A. Karwoski" 4 => "R. Clay" 5 => "R.A. Robb" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1371/journal.pone.0196910" "Revista" => array:5 [ "tituloSerie" => "PLOS ONE" "fecha" => "2018" "volumen" => "13" "paginaInicial" => "e0196910" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29758038" "web" => "Medline" ] ] ] ] ] ] ] ] 9 => array:3 [ "identificador" => "bib0245" "etiqueta" => "10" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A.M. García-Vicente" 1 => "D. Molina" 2 => "J. Pérez-Beteta" 3 => "M. Amo-Salas" 4 => "A. Martínez-González" 5 => "G. Bueno" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s12149-017-1203-2" "Revista" => array:6 [ "tituloSerie" => "Ann Nucl Med" "fecha" => "2017" "volumen" => "31" "paginaInicial" => "726" "paginaFinal" => "735" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28887761" "web" => "Medline" ] ] ] ] ] ] ] ] 10 => array:3 [ "identificador" => "bib0250" "etiqueta" => "11" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "M. Hatt" 1 => "B. Laurent" 2 => "H. Fayad" 3 => "V. Jaouen" 4 => "D. Visvikis" 5 => "C.C. Le Rest" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-017-3865-3" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2018" "volumen" => "45" "paginaInicial" => "630" "paginaFinal" => "641" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29177871" "web" => "Medline" ] ] ] ] ] ] ] ] 11 => array:3 [ "identificador" => "bib0255" "etiqueta" => "12" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Metabolic radiomics for pretreatment 18F-FDG PET/CT to characterize locally advanced breast cancer: histopathologic characteristics, response to neoadjuvant chemotherapy, and prognosis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "S. Ha" 1 => "S. Park" 2 => "J.I. Bang" 3 => "E.K. Kim" 4 => "H.Y. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1038/s41598-017-01524-7" "Revista" => array:5 [ "tituloSerie" => "Sci Rep" "fecha" => "2017" "volumen" => "7" "paginaInicial" => "1556" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28484211" "web" => "Medline" ] ] ] ] ] ] ] ] 12 => array:3 [ "identificador" => "bib0260" "etiqueta" => "13" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "[18F]FDG PET/CT features for the molecular characterization of primary breast tumors" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "L. Antunovic" 1 => "F. Gallivanone" 2 => "M. Sollini" 3 => "A. Sagona" 4 => "A. Invento" 5 => "G. Manfrinato" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-017-3770-9" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2017" "volumen" => "44" "paginaInicial" => "1945" "paginaFinal" => "1954" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28711994" "web" => "Medline" ] ] ] ] ] ] ] ] 13 => array:3 [ "identificador" => "bib0265" "etiqueta" => "14" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Radiomics in oncological PET/CT: clinical applications" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "J.W. Lee" 1 => "S.M. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s13139-017-0500-y" "Revista" => array:7 [ "tituloSerie" => "Nucl Med Mol Imaging" "fecha" => "2018" "volumen" => "52" "paginaInicial" => "170" "paginaFinal" => "189" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29942396" "web" => "Medline" ] ] "itemHostRev" => array:3 [ "pii" => "S0959804911004679" "estado" => "S300" "issn" => "09598049" ] ] ] ] ] ] ] 14 => array:3 [ "identificador" => "bib0270" "etiqueta" => "15" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Towards precision medicine: from quantitative imaging to radiomics" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "U.R. Acharya" 1 => "Y. Hagiwara" 2 => "V.K. Sudarshan" 3 => "W.Y. Chan" 4 => "K.H. Ng" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1631/jzus.B1700260" "Revista" => array:6 [ "tituloSerie" => "J Zhejiang Univ Sci B" "fecha" => "2018" "volumen" => "19" "paginaInicial" => "6" "paginaFinal" => "24" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29308604" "web" => "Medline" ] ] ] ] ] ] ] ] 15 => array:3 [ "identificador" => "bib0275" "etiqueta" => "16" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Textural parameters of tumor heterogeneity in <span class="elsevierStyleSup">18</span>F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "R.A. Bundschuh" 1 => "J. Dinges" 2 => "L. Neumann" 3 => "M. Seyfried" 4 => "N. Zsótér" 5 => "L. Papp" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.2967/jnumed.113.127340" "Revista" => array:6 [ "tituloSerie" => "J Nucl Med" "fecha" => "2014" "volumen" => "55" "paginaInicial" => "891" "paginaFinal" => "897" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24752672" "web" => "Medline" ] ] ] ] ] ] ] ] 16 => array:3 [ "identificador" => "bib0280" "etiqueta" => "17" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Prognostic value and clinical correlations of 18-fluorodeoxyglucose metabolism quantifiers in gastric cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "K. Grabinska" 1 => "M. Pelak" 2 => "J. Wydmanski" 3 => "A. Tukiendorf" 4 => "A. d’Amico" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.3748/wjg.v21.i19.5901" "Revista" => array:6 [ "tituloSerie" => "World J Gastroenterol" "fecha" => "2015" "volumen" => "21" "paginaInicial" => "5901" "paginaFinal" => "5909" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26019454" "web" => "Medline" ] ] ] ] ] ] ] ] 17 => array:3 [ "identificador" => "bib0285" "etiqueta" => "18" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Increased evidence for the prognostic value of primary tumor asphericity in pretherapeutic FDG PET for risk stratification in patients with head and neck cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "F. Hofheinz" 1 => "A. Lougovski" 2 => "K. Zöphel" 3 => "M. Hentschel" 4 => "I.G. Steffen" 5 => "I. Apostolova" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-014-2953-x" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2015" "volumen" => "42" "paginaInicial" => "429" "paginaFinal" => "437" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25416633" "web" => "Medline" ] ] ] ] ] ] ] ] 18 => array:3 [ "identificador" => "bib0290" "etiqueta" => "19" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "I. Apostolova" 1 => "J. Rogasch" 2 => "R. Buchert" 3 => "H. Wertzel" 4 => "H.J. Achenbach" 5 => "J. Schreiber" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1186/1471-2407-14-896" "Revista" => array:6 [ "tituloSerie" => "BMC Cancer" "fecha" => "2014" "volumen" => "14" "paginaInicial" => "896" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25444154" "web" => "Medline" ] ] "itemHostRev" => array:3 [ "pii" => "S1462388918300206" "estado" => "S300" "issn" => "14623889" ] ] ] ] ] ] ] 19 => array:3 [ "identificador" => "bib0295" "etiqueta" => "20" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Asphericity of pretherapeutic tumor FDG uptake provides independent prognostic value in head-and-neck cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "I. Apostolova" 1 => "I.G. Steffen" 2 => "F. Wedel" 3 => "A. Lougovski" 4 => "T. Derlin" 5 => "S. Marnitz" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00330-014-3269-8" "Revista" => array:6 [ "tituloSerie" => "Eur Radiol" "fecha" => "2014" "volumen" => "24" "paginaInicial" => "2077" "paginaFinal" => "2087" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24965509" "web" => "Medline" ] ] ] ] ] ] ] ] 20 => array:3 [ "identificador" => "bib0300" "etiqueta" => "21" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The asphericity of the metabolic tumour volume in NSCLC: correlation with histopathology and molecular markers" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "I. Apostolova" 1 => "K. Ego" 2 => "I.G. Steffen" 3 => "R. Buchert" 4 => "H. Wertzel" 5 => "H.J. Achenbach" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-016-3452-z" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2016" "volumen" => "43" "paginaInicial" => "2360" "paginaFinal" => "2373" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27470327" "web" => "Medline" ] ] ] ] ] ] ] ] 21 => array:3 [ "identificador" => "bib0305" "etiqueta" => "22" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "F. Tixier" 1 => "M. Hatt" 2 => "C. Valla" 3 => "V. Fleury" 4 => "C. Lamour" 5 => "S. Ezzouhri" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.2967/jnumed.113.133389" "Revista" => array:6 [ "tituloSerie" => "J Nucl Med" "fecha" => "2014" "volumen" => "55" "paginaInicial" => "1235" "paginaFinal" => "1241" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24904113" "web" => "Medline" ] ] ] ] ] ] ] ] 22 => array:3 [ "identificador" => "bib0310" "etiqueta" => "23" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Quantification of intratumoral metabolic macroheterogeneity on 18F-FDG PET/CT and its prognostic significance in pathologic N0 squamous cell lung carcinoma" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D.H. Kim" 1 => "J.H. Jung" 2 => "S.H. Son" 3 => "C.Y. Kim" 4 => "S.Y. Jeong" 5 => "S.W. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1097/RLU.0000000000000930" "Revista" => array:6 [ "tituloSerie" => "Clin Nucl Med" "fecha" => "2016" "volumen" => "41" "paginaInicial" => "e70" "paginaFinal" => "e75" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26284762" "web" => "Medline" ] ] ] ] ] ] ] ] 23 => array:3 [ "identificador" => "bib0315" "etiqueta" => "24" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "CONSORT-independent prognostic value of asphericity of pretherapeutic F-18 FDG uptake by primary tumors in patients with breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J.H. Jung" 1 => "S.H. Son" 2 => "D.H. Kim" 3 => "J. Lee" 4 => "S.Y. Jeong" 5 => "S.W. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:4 [ "tituloSerie" => "Medicine (Baltimore)" "fecha" => "2017" "volumen" => "96" "paginaInicial" => "e8438" ] ] ] ] ] ] 24 => array:3 [ "identificador" => "bib0320" "etiqueta" => "25" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "T.P. Coroller" 1 => "V. Agrawal" 2 => "E. Huynh" 3 => "V. Narayan" 4 => "S.W. Lee" 5 => "R.H. Mak" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.jtho.2016.11.2226" "Revista" => array:6 [ "tituloSerie" => "J Thorac Oncol" "fecha" => "2017" "volumen" => "12" "paginaInicial" => "467" "paginaFinal" => "476" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27903462" "web" => "Medline" ] ] ] ] ] ] ] ] 25 => array:3 [ "identificador" => "bib0325" "etiqueta" => "26" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Glycolytic activity with 18F-FDG PET/CT predicts final neoadjuvant chemotherapy response in breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A.M. García Vicente" 1 => "Cruz Mora MÁ" 2 => "A.A. León Martín" 3 => "M. Muñoz Sánchez" 4 => "M. del" 5 => "F. Relea Calatayud" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s13277-014-2495-7" "Revista" => array:6 [ "tituloSerie" => "Tumour Biol" "fecha" => "2014" "volumen" => "35" "paginaInicial" => "11613" "paginaFinal" => "11620" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25139100" "web" => "Medline" ] ] ] ] ] ] ] ] 26 => array:3 [ "identificador" => "bib0330" "etiqueta" => "27" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Molecular subtypes of breast cancer: metabolic correlation with 18 F-FDG PET/CT" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A.M. García Vicente" 1 => "Á. Soriano Castrejón" 2 => "A. León Martín" 3 => "I. Chacón López-Muñiz" 4 => "V. Muñoz Madero" 5 => "M. Muñoz Sánchez" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-013-2418-7" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2013" "volumen" => "40" "paginaInicial" => "1304" "paginaFinal" => "1311" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/23632960" "web" => "Medline" ] ] ] ] ] ] ] ] 27 => array:3 [ "identificador" => "bib0335" "etiqueta" => "28" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Schaefer" 1 => "Y.J. Kim" 2 => "S. Kremp" 3 => "S. Mai" 4 => "J. Fleckenstein" 5 => "H. Bohnenberger" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-013-2407-x" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2013" "volumen" => "40" "paginaInicial" => "1233" "paginaFinal" => "1244" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/23632957" "web" => "Medline" ] ] ] ] ] ] ] ] 28 => array:3 [ "identificador" => "bib0340" "etiqueta" => "29" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Tumor surface regularity at MR imaging predicts survival and response to surgery in patients with glioblastoma" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. Pérez-Beteta" 1 => "D. Molina-García" 2 => "J.A. Ortiz-Alhambra" 3 => "A. Fernández-Romero" 4 => "B. Luque" 5 => "E. Arregui" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1148/radiol.2018171051" "Revista" => array:7 [ "tituloSerie" => "Radiology" "fecha" => "2018" "volumen" => "288" "paginaInicial" => "218" "paginaFinal" => "225" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29924716" "web" => "Medline" ] ] "itemHostRev" => array:3 [ "pii" => "S0268960X09000642" "estado" => "S300" "issn" => "0268960X" ] ] ] ] ] ] ] 29 => array:3 [ "identificador" => "bib0345" "etiqueta" => "30" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "H. Kim" 1 => "C.M. Park" 2 => "M. Lee" 3 => "S.J. Park" 4 => "Y.S. Song" 5 => "J.H. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1371/journal.pone.0164924" "Revista" => array:5 [ "tituloSerie" => "PLOS ONE" "fecha" => "2016" "volumen" => "11" "paginaInicial" => "e0164924" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27741289" "web" => "Medline" ] ] ] ] ] ] ] ] 30 => array:3 [ "identificador" => "bib0350" "etiqueta" => "31" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "L. Alic" 1 => "W.J. Niessen" 2 => "J.F. Veenland" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1371/journal.pone.0110300" "Revista" => array:5 [ "tituloSerie" => "PLOS ONE" "fecha" => "2014" "volumen" => "9" "paginaInicial" => "e110300" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25330171" "web" => "Medline" ] ] ] ] ] ] ] ] 31 => array:3 [ "identificador" => "bib0355" "etiqueta" => "32" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D. Molina" 1 => "J. Pérez-Beteta" 2 => "A. Martínez-González" 3 => "J. Martino" 4 => "C. Velasquez" 5 => "E. Arana" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1371/journal.pone.0178843" "Revista" => array:5 [ "tituloSerie" => "PLOS ONE" "fecha" => "2017" "volumen" => "12" "paginaInicial" => "e0178843" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28586353" "web" => "Medline" ] ] ] ] ] ] ] ] 32 => array:3 [ "identificador" => "bib0360" "etiqueta" => "33" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Hypoxia and hypoxia-inducible factors: master regulators of metastasis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "X. Lu" 1 => "Y. Kang" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1158/1078-0432.CCR-10-1360" "Revista" => array:6 [ "tituloSerie" => "Clin Cancer Res" "fecha" => "2010" "volumen" => "16" "paginaInicial" => "5928" "paginaFinal" => "5935" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/20962028" "web" => "Medline" ] ] ] ] ] ] ] ] 33 => array:3 [ "identificador" => "bib0365" "etiqueta" => "34" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "M. Soussan" 1 => "F. Orlhac" 2 => "M. Boubaya" 3 => "L. Zelek" 4 => "M. Ziol" 5 => "V. Eder" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1371/journal.pone.0094017" "Revista" => array:5 [ "tituloSerie" => "PLOS ONE" "fecha" => "2014" "volumen" => "9" "paginaInicial" => "e94017" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24722644" "web" => "Medline" ] ] ] ] ] ] ] ] 34 => array:3 [ "identificador" => "bib0370" "etiqueta" => "35" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Texture analysis of high-resolution dedicated breast 18 F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Moscoso" 1 => "Á. Ruibal" 2 => "I. Domínguez-Prado" 3 => "A. Fernández-Ferreiro" 4 => "M. Herranz" 5 => "L. Albaina" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-017-3830-1" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2018" "volumen" => "45" "paginaInicial" => "196" "paginaFinal" => "206" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28936601" "web" => "Medline" ] ] ] ] ] ] ] ] 35 => array:3 [ "identificador" => "bib0375" "etiqueta" => "36" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D. Molina-García" 1 => "A.M. García-Vicente" 2 => "J. Pérez-Beteta" 3 => "M. Amo-Salas" 4 => "A. Martínez-González" 5 => "M.J. Tello-Galán" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s12149-018-1253-0" "Revista" => array:6 [ "tituloSerie" => "Ann Nucl Med" "fecha" => "2018" "volumen" => "32" "paginaInicial" => "379" "paginaFinal" => "388" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29869770" "web" => "Medline" ] ] ] ] ] ] ] ] 36 => array:3 [ "identificador" => "bib0380" "etiqueta" => "37" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Fluorine-18 fluorodeoxyglucose positron emission tomography-computed tomography in monitoring the response of breast cancer to neoadjuvant chemotherapy: a meta-analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "F.P. Mghanga" 1 => "X. Lan" 2 => "K.H. Bakari" 3 => "C. Li" 4 => "Y. Zhang" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.clbc.2013.02.003" "Revista" => array:6 [ "tituloSerie" => "Clin Breast Cancer" "fecha" => "2013" "volumen" => "13" "paginaInicial" => "271" "paginaFinal" => "279" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/23714689" "web" => "Medline" ] ] ] ] ] ] ] ] 37 => array:3 [ "identificador" => "bib0385" "etiqueta" => "38" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Role of positron emission tomography for the monitoring of response to therapy in breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "O. Humbert" 1 => "A. Cochet" 2 => "B. Coudert" 3 => "A. Berriolo-Riedinger" 4 => "S. Kanoun" 5 => "F. Brunotte" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1634/theoncologist.2014-0342" "Revista" => array:6 [ "tituloSerie" => "Oncologist" "fecha" => "2015" "volumen" => "20" "paginaInicial" => "94" "paginaFinal" => "104" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25561512" "web" => "Medline" ] ] ] ] ] ] ] ] 38 => array:3 [ "identificador" => "bib0390" "etiqueta" => "39" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D. Groheux" 1 => "A. Martineau" 2 => "L. Teixeira" 3 => "M. Espié" 4 => "P. de Cremoux" 5 => "P. Bertheau" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1186/s13058-016-0793-2" "Revista" => array:5 [ "tituloSerie" => "Breast Cancer Res" "fecha" => "2017" "volumen" => "19" "paginaInicial" => "3" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28057031" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] ] "idiomaDefecto" => "en" "url" => "/22538089/0000003800000005/v1_201909030619/S2253808919300400/v1_201909030619/en/main.assets" "Apartado" => array:4 [ "identificador" => "34047" "tipo" => "SECCION" "en" => array:2 [ "titulo" => "Original article" "idiomaDefecto" => true ] "idiomaDefecto" => "en" ] "PDF" => "https://static.elsevier.es/multimedia/22538089/0000003800000005/v1_201909030619/S2253808919300400/v1_201909030619/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808919300400?idApp=UINPBA00004N" ]
Journal Information
Share
Download PDF
More article options
Original Article
Global heterogeneity assessed with 18F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer
Medidas de heterogeneidad global y esfericidad con 18F-FDG PET/TC en el cáncer de mama: relación con biología tumoral, valor predictivo y pronóstico