Share
array:24 [ "pii" => "S2253808917302070" "issn" => "22538089" "doi" => "10.1016/j.remnie.2017.12.003" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "939" "copyright" => "Elsevier España, S.L.U. y SEMNIM" "copyrightAnyo" => "2017" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2018;37:73-9" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 4 "formatos" => array:2 [ "HTML" => 2 "PDF" => 2 ] ] "Traduccion" => array:1 [ "es" => array:19 [ "pii" => "S2253654X17301300" "issn" => "2253654X" "doi" => "10.1016/j.remn.2017.09.002" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "939" "copyright" => "Elsevier España, S.L.U. y SEMNIM" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2018;37:73-9" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 635 "formatos" => array:2 [ "HTML" => 410 "PDF" => 225 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Papel predictivo y pronóstico de las variables volumétricas metabólicas obtenidas en la <span class="elsevierStyleSup">18</span>F-FDG PET/TC en el cáncer de mama con indicación de quimioterapia neoadyuvante" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "73" "paginaFinal" => "79" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Predictive and prognostic potential of volume-based metabolic variables obtained by a baseline <span class="elsevierStyleSup">18</span>F-FDG PET/CT in breast cancer with neoadjuvant chemotherapy indication" ] ] "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" => "fig0015" "etiqueta" => "Figura 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1047 "Ancho" => 2520 "Tamanyo" => 102229 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">El análisis de Kaplan-Meier utilizando un punto de corte de TLG de 59,05 (A) y 36,17 (B) muestra asociación significativa con la OS. Los detalles del punto de corte se muestran en la <a class="elsevierStyleCrossRef" href="#tbl0015">tabla 3</a>.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "A.M. Garcia-Vicente, J. Pérez-Beteta, M. Amo-Salas, D. Molina, G.A. Jimenez-Londoño, A.M. Soriano-Castrejón, F.J. Pena Pardo, A. Martínez-González" "autores" => array:8 [ 0 => array:2 [ "nombre" => "A.M." "apellidos" => "Garcia-Vicente" ] 1 => array:2 [ "nombre" => "J." "apellidos" => "Pérez-Beteta" ] 2 => array:2 [ "nombre" => "M." "apellidos" => "Amo-Salas" ] 3 => array:2 [ "nombre" => "D." "apellidos" => "Molina" ] 4 => array:2 [ "nombre" => "G.A." "apellidos" => "Jimenez-Londoño" ] 5 => array:2 [ "nombre" => "A.M." "apellidos" => "Soriano-Castrejón" ] 6 => array:2 [ "nombre" => "F.J." "apellidos" => "Pena Pardo" ] 7 => array:2 [ "nombre" => "A." "apellidos" => "Martínez-González" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2253808917302070" "doi" => "10.1016/j.remnie.2017.12.003" "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/S2253808917302070?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X17301300?idApp=UINPBA00004N" "url" => "/2253654X/0000003700000002/v1_201803020432/S2253654X17301300/v1_201803020432/es/main.assets" ] ] "itemSiguiente" => array:19 [ "pii" => "S225380891730157X" "issn" => "22538089" "doi" => "10.1016/j.remnie.2017.10.021" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "932" "copyright" => "Elsevier España, S.L.U. y SEMNIM" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2018;37:80-6" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 8 "formatos" => array:2 [ "HTML" => 3 "PDF" => 5 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Correlación de la captación de <span class="elsevierStyleSup">18</span>F-FDG de la PET/TC con el Ki67 de la inmunohistoquímica en el cáncer epitelial de ovario pretratamiento" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "80" "paginaFinal" => "86" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Correlation of <span class="elsevierStyleSup">18</span>F-FDG uptake on PET/CT with Ki67 immunohistochemistry in pre-treatment epithelial ovarian 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" => "fig0005" "etiqueta" => "Figura 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1703 "Ancho" => 2541 "Tamanyo" => 198244 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Adquisición del fantoma. Imágenes de TC axial (A), fusión PET/TC (B) y PET (C) realizadas en el fantoma (D) utilizado en el estudio de simulación. Este fantoma contiene 6 esferas de diferentes volúmenes (diámetros): 0,5<span class="elsevierStyleHsp" style=""></span>mL (10<span class="elsevierStyleHsp" style=""></span>mm), 1,2<span class="elsevierStyleHsp" style=""></span>mL (13<span class="elsevierStyleHsp" style=""></span>mm), 2,6<span class="elsevierStyleHsp" style=""></span>mL (17<span class="elsevierStyleHsp" style=""></span>mm), 5,6<span class="elsevierStyleHsp" style=""></span>mL (22<span class="elsevierStyleHsp" style=""></span>mm), 11,5<span class="elsevierStyleHsp" style=""></span>mL (28<span class="elsevierStyleHsp" style=""></span>mm) y 26,5<span class="elsevierStyleHsp" style=""></span>mL (37<span class="elsevierStyleHsp" style=""></span>mm).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "M. Mayoral, P. Paredes, A. Saco, P. Fusté, P. Perlaza, A. Tapias, A. Fernandez-Martinez, L. Vidal, J. Ordi, J. Pavia, S. Martinez-Roman, F. Lomeña" "autores" => array:12 [ 0 => array:2 [ "nombre" => "M." "apellidos" => "Mayoral" ] 1 => array:2 [ "nombre" => "P." "apellidos" => "Paredes" ] 2 => array:2 [ "nombre" => "A." "apellidos" => "Saco" ] 3 => array:2 [ "nombre" => "P." "apellidos" => "Fusté" ] 4 => array:2 [ "nombre" => "P." "apellidos" => "Perlaza" ] 5 => array:2 [ "nombre" => "A." "apellidos" => "Tapias" ] 6 => array:2 [ "nombre" => "A." "apellidos" => "Fernandez-Martinez" ] 7 => array:2 [ "nombre" => "L." "apellidos" => "Vidal" ] 8 => array:2 [ "nombre" => "J." "apellidos" => "Ordi" ] 9 => array:2 [ "nombre" => "J." "apellidos" => "Pavia" ] 10 => array:2 [ "nombre" => "S." "apellidos" => "Martinez-Roman" ] 11 => array:2 [ "nombre" => "F." "apellidos" => "Lomeña" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X17300616" "doi" => "10.1016/j.remn.2017.07.005" "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/S2253654X17300616?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S225380891730157X?idApp=UINPBA00004N" "url" => "/22538089/0000003700000002/v1_201803020417/S225380891730157X/v1_201803020417/es/main.assets" ] "itemAnterior" => array:18 [ "pii" => "S2253808918300028" "issn" => "22538089" "doi" => "10.1016/j.remnie.2017.12.008" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "966" "documento" => "simple-article" "crossmark" => 1 "subdocumento" => "edi" "cita" => "Rev Esp Med Nucl Imagen Mol. 2018;37:71-2" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 4 "formatos" => array:2 [ "HTML" => 2 "PDF" => 2 ] ] "en" => array:10 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Editorial</span>" "titulo" => "Evolution of nuclear medicine in the diagnosis and treatment of prostate cancer" "tienePdf" => "en" "tieneTextoCompleto" => "en" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "71" "paginaFinal" => "72" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Evolución de la Medicina Nuclear en el diagnóstico y tratamiento de pacientes con cáncer de próstata" ] ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "M.J. García Velloso, M. Rodríguez Fraile" "autores" => array:2 [ 0 => array:2 [ "nombre" => "M.J." "apellidos" => "García Velloso" ] 1 => array:2 [ "nombre" => "M." "apellidos" => "Rodríguez Fraile" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X17303153" "doi" => "10.1016/j.remn.2017.12.007" "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/S2253654X17303153?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808918300028?idApp=UINPBA00004N" "url" => "/22538089/0000003700000002/v1_201803020417/S2253808918300028/v1_201803020417/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Predictive and prognostic potential of volume-based metabolic variables obtained by a baseline <span class="elsevierStyleSup">18</span>F-FDG PET/CT in breast cancer with neoadjuvant chemotherapy indication" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "73" "paginaFinal" => "79" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "A.M. Garcia-Vicente, J. Pérez-Beteta, M. Amo-Salas, D. Molina, G.A. Jimenez-Londoño, A.M. Soriano-Castrejón, F.J. Pena Pardo, A. Martínez-González" "autores" => array:8 [ 0 => array:4 [ "nombre" => "A.M." "apellidos" => "Garcia-Vicente" "email" => array:1 [ 0 => "angarvice@yahoo.es" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "J." "apellidos" => "Pérez-Beteta" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] 2 => array:3 [ "nombre" => "M." "apellidos" => "Amo-Salas" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] 3 => array:3 [ "nombre" => "D." "apellidos" => "Molina" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] 4 => array:3 [ "nombre" => "G.A." "apellidos" => "Jimenez-Londoño" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] 5 => array:3 [ "nombre" => "A.M." "apellidos" => "Soriano-Castrejón" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] 6 => array:3 [ "nombre" => "F.J." "apellidos" => "Pena Pardo" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] 7 => array:3 [ "nombre" => "A." "apellidos" => "Martínez-González" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn0005" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Nuclear Medicine Department, Hospital General Universitario de Ciudad Real, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Ciudad Real, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Department of Mathematics, Universidad de Castilla-La Mancha, 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" => "Papel predictivo y pronóstico de las variables volumétricas metabólicas obtenidas en la <span class="elsevierStyleSup">18</span>F-FDG PET/TC en cáncer de mama con indicación de quimioterapia adyuvante" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1617 "Ancho" => 3084 "Tamanyo" => 263673 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">52-year-old female with triple negative invasive ductal carcinoma, stage T2NxM1 by <span class="elsevierStyleSup">18</span>F-FDG PET/CT. (A) Maximum intensity projection of a <span class="elsevierStyleSup">18</span>F-FDG-PET/CT image that represents a left breast tumor and multiple physiological brown fat deposits in the cervical and paraspinal regions. (B) Axial <span class="elsevierStyleSup">18</span>F-FDG-PET/CT, (C) CT and (D) co-registration of these that show a right lung nodule with FDG avidity. (E) Breast tumor in <span class="elsevierStyleSup">18</span>F-FDG-PET/CT and CT co-registered axial projection. (F) Breast tumor voxels after 3D segmentation using a code developed and implemented in-house at MATLAB and (G) lesion reconstruction. After neoadjuvant chemotherapy no complete histological response of breast tumor was achieved although the pulmonary nodule disappeared. Patient was in DFs during follow-up (OS and DFS of 40 months).</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">In breast cancer (BC), based on the associations of molecular features with prognosis, even with neoadjuvant chemotherapy (NC) response, the goal of baseline fluorine-18 fluorodeoxyglucose (<span class="elsevierStyleSup">18</span>F-FDG) uptake on PET/CT, as a biomarker, has been explored in order to find an earlier image predictor.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">1</span></a> However, the association of glycolytic metabolism with NC response and prognosis in BC is controversial.<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">2–7</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Regarding prognosis, some studies have found significant relations, determining that a high tumor uptake of <span class="elsevierStyleSup">18</span>F-FDG can serve as a risk factor for recurrence and death in patients with BC<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">2–4</span></a> while other authors have not reported these differences.<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">5–7</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">On the other hand, no consensus exists on the predictive value of SUVmax in the NC setting, with differences in the results reported in the literature.<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">8–10</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">With respect to the semiquantitative approach, other metrics of metabolism, apart from SUVmax can be calculated, such as the average uptake in the tumor (SUVmean) or the maximum average SUV in a cube of 3<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>3<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>3 voxels (SUVpeak). Further, an approximation of tumor burden has been introduced with volumetric variables, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG).<a class="elsevierStyleCrossRefs" href="#bib0205"><span class="elsevierStyleSup">11–12</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">It could be hypothesized that volume-based metabolic variables would be likely to offer a more integrated tumor information that would help explain the discordances found regarding the predictive and prognostic value of with the SUV-based variables. Nevertheless, there is limited reported experience on the predictive and prognostic value of volume-based metabolic variables obtained on the basal <span class="elsevierStyleSup">18</span>F-FDG PET/CT.<a class="elsevierStyleCrossRefs" href="#bib0205"><span class="elsevierStyleSup">11–12</span></a> The objective of the present study was to investigate the prognostic and predictive value of all available metrics based on the metabolic information of basal <span class="elsevierStyleSup">18</span>F-FDG PET/CT in patients with BC with NC indication.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials and methods</span><p id="par0030" class="elsevierStylePara elsevierViewall">This prospective multi-center study was approved by the local ethics committee and Research Board of our Institution and included 7 hospitals. The Institutional Review Boards at each hospital approved the study.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Patients</span><p id="par0035" class="elsevierStylePara elsevierViewall">Patients reported in this study were participants in an ongoing prospective study initiated in September 2009. The end date of recruitment was June 2014. The inclusion criteria were: to have a newly diagnosed BC with clinical indication of NC and absence of distant metastases previously confirmed by other methods, to request the <span class="elsevierStyleSup">18</span>F-FDG PET/CT for staging purposes and to have obtained written informed consent from all patients.</p><p id="par0040" class="elsevierStylePara elsevierViewall">For the present analysis, patients with a unique or predominant breast lesion on the <span class="elsevierStyleSup">18</span>F-FDG PET/CT scan, with an uptake higher than background and a clinical size of at least 2<span class="elsevierStyleHsp" style=""></span>cm greatest diameter in any projection on conventional imaging (ultrasonography or mammography) were selected.</p><p id="par0045" class="elsevierStylePara elsevierViewall">With respect to the NC protocol, all patients received a standard, approved regimen, which consisted in a combination of anthracyclines, taxanes and anti-HER2 therapy referred to in a previous publication.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">13</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">After surgery, adjuvant treatment with/without radiotherapy was administered based on post-NC stage and tumor biology.</p><p id="par0055" class="elsevierStylePara elsevierViewall">Patients underwent standard follow-up of a minimum of 24 months. Disease free survival (DFS) was defined as the time, in months, from the date of initial staging to tumor recurrence or death from any cause. Overall survival (OS) was defined as the time, in months, from the date of initial staging to death from any cause or the date of censoring of the last time a patient is observed. Furthermore, after follow up, patient status was established as disease free (DFs) or non-DFs. The latter included death, stable disease, partial response, recurrence or progression during follow-up.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Pre-treatment and post-treatment histopathological analysis</span><p id="par0060" class="elsevierStylePara elsevierViewall">The histopathological analysis of the primary tumor was performed on specimens obtained by core aspiration biopsy. The determination of tumor type and the histopathological grading were obtained following the specifications of a previous publication.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">10</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">Immunohistochemistry was performed on paraffin-embedded material using primary antibodies for estrogen and progesterone receptors (ER/PR), human epidermal growth factor receptor (HER2) and the proliferation index based on the Ki-67 antibody. ER, PR and HER2 were scored as positive (+) or negative (−) as previously referenced by our group.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">10</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">Final positive lymph node status (positive or negative) was established by the clinician attending to histopathological confirmation by fine needle aspiration biopsy (FNAB)/sentinel lymph node biopsy (SLNB) or in cases of multiple pathologic lymph nodes, with the use of ultrasonography and <span class="elsevierStyleSup">18</span>F-FDG PET/CT information.</p><p id="par0075" class="elsevierStylePara elsevierViewall">With respect to the different combinations of ER, PR and HER2 status and Ki-67 labeling index, patients were categorized into five molecular phenotypes [luminal A, luminal B-HER2(−), luminal B-HER2(+), HER2(+) pure and triple negative].<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">14</span></a> Furthermore, patients were classified into risk categories depending on tumor phenotype: high risk [triple negative or HER2(+) pure], intermediate risk [luminal B-HER2(−) or luminal B-HER2(+)] and low risk [Luminal A].</p><p id="par0080" class="elsevierStylePara elsevierViewall">All patients underwent mastectomy or quadrantectomy and axillary lymph node dissection (ALND) 4–6 weeks after NC.</p><p id="par0085" class="elsevierStylePara elsevierViewall">Breast and lymph nodes specimens, were surgically removed, sliced, prepared and analyzed. For this study, only a binary breast histological response was considered classifying the lesions as responders or non-responders as previously stated.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">10</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085"><span class="elsevierStyleSup">18</span>F-FDG-PET/CT imaging acquisition and interpretation</span><p id="par0090" class="elsevierStylePara elsevierViewall">Patients fasted for at least 6<span class="elsevierStyleHsp" style=""></span>h before the <span class="elsevierStyleSup">18</span>F-FDG-PET/CT examination and had blood glucose levels below 160<span class="elsevierStyleHsp" style=""></span>mg/dl at the time of injection. <span class="elsevierStyleSup">18</span>F-FDG-PET/CT was performed on the same dedicated whole-body PET/CT machine (Discovery DSTE-16 s, GE Medical Systems) following a standardized protocol in three-dimensional (3D) mode.</p><p id="par0095" class="elsevierStylePara elsevierViewall">For the visual evaluation, any increased uptake local (axillary lymph nodes) or distant (extra-axillary lymph nodes and or visceral locations) increased uptake to the primary tumor and not explained by a physiological or inflammatory process was considered to be pathological.</p><p id="par0100" class="elsevierStylePara elsevierViewall">Two experienced nuclear medicine physicians independently assessed the <span class="elsevierStyleSup">18</span>F-FDG-PET/CT scans. In case of disagreement third physician evaluated the images.</p><p id="par0105" class="elsevierStylePara elsevierViewall">The final stage was established, integrating histology and <span class="elsevierStyleSup">18</span>F-FDG-PET/CT information, according to the 7th edition of the American Joint Committee on Cancer (AJCC) classification.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">15</span></a> When a SLNB was performed previous to <span class="elsevierStyleSup">18</span>F-FDG-PET/CT, the metabolic stage could not be established.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Lesion segmentation and analysis</span><p id="par0110" class="elsevierStylePara elsevierViewall">For the semiquantitative assessment, the DICOM (Digital Imaging and Communication in Medicine) files of the <span class="elsevierStyleSup">18</span>F-FDG-PET/CT images were imported into the scientific software package MATLAB (R2015b, The MathWorks, Inc., Natick, MA, USA) and pre-processed using a semi-automatic image segmentation procedure. All image visualizing and processing procedures were performed using a code developed and implemented in-house at MATLAB.</p><p id="par0115" class="elsevierStylePara elsevierViewall">After semi-automatic segmentation, SUV was calculated through the formula: tissue concentration (MBq/g)/injected dose (MBq)/body weight (g). The application automatically obtains the greatest SUV within the tumor box, which is the SUVmax3D (called SUVmax to simplify). The regions in the box equal to or above 40% of 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 cases of multiple breast lesions (multi-center or multi-focal cancer) the biggest one with the highest FDG uptake was selected for analysis.</p><p id="par0120" class="elsevierStylePara elsevierViewall">After segmentation, the other metabolic and volumetric variables were calculated for each patient. SUVmean was measured as the average uptake in the tumor VOI. SUVpeak was computed as the maximum average SUV in a cube of 3<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>3<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>3 voxels included in the tumor for each possible location of this cube in the VOI. MTV was the volume of the VOI obtained by each segmentation method. TLG was calculated as the product of SUVmean and MTV.</p><p id="par0125" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> shows an example of the tumor volume obtained after segmentation.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Statistical analysis</span><p id="par0130" class="elsevierStylePara elsevierViewall">Phenotype risk and metabolic stage variables were converted into categorical variables. The phenotype risk was divided into two groups: high risk [basal-like and HER2(+) pure] and low/intermediate risk [luminal B-HER2(−), luminal B-HER2(+) and Luminal A]. For the <span class="elsevierStyleSup">18</span>F-FDG-PET/CT stage, the IV stage was evaluated against the other stages (II or III).</p><p id="par0135" class="elsevierStylePara elsevierViewall">Relations of SUV and volume-based variables with NC response and DFs were studied using Kruskal–Wallis test and receiver operator characteristics (ROC) analysis. We also computed the area under the curve (AUC) and the optimum threshold considered as the cut-off value: the furthest point on the curve from the line of no-discrimination. The Kolmogorov–Smirnov test was used for checking the normality of data.</p><p id="par0140" class="elsevierStylePara elsevierViewall">Survival analysis was performed using Kaplan–Meier and Cox regression methods. In the case of Cox regression a stepwise method was used.</p><p id="par0145" class="elsevierStylePara elsevierViewall">A significance level (<span class="elsevierStyleItalic">p</span>-value) of <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05 was used in all statistical tests.</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Results</span><p id="par0150" class="elsevierStylePara elsevierViewall">Sixty out off 67 patients underwent NC. 14 were classified as responders. 54 patients had DFs during the follow-up. The 5-year OS and DFS rates obtained from the Kaplan–Meier analysis were 85.6% and 75.5% respectively. The patient characteristics are shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0155" class="elsevierStylePara elsevierViewall">Median<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD DFS and OS was 43<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15 and 46<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13 months respectively.</p><p id="par0160" class="elsevierStylePara elsevierViewall">SUV and TLG showed a significant relation (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.005) to the histological response with higher values in responders compared to non-responders. MTV and TLG showed significant association with DFs, patients with DFs showing lower values of these variables. No significant relations between SUV variables and DFs were observed. The results are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0165" class="elsevierStylePara elsevierViewall">With respect to the ROC analysis of SUV and volume-based metabolic variables in the prediction of NC response, associations of SUV-based variables and TLG with NC response were found. A SUVmax cut-off of 7.9 showed a sensitivity of 79% and a specificity of 69% in the prediction of NC response. With regard to the prognosis, only volume-based variables showed association with DFs. <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows the results.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0170" class="elsevierStylePara elsevierViewall">NC response was not associated with OS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.620) nor with DFS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.730) in the Kaplan–Meier analysis. MTV was significantly associated with OS and DFS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001, <span class="elsevierStyleItalic">χ</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>10.82 and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.008, <span class="elsevierStyleItalic">χ</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>7.14 respectively (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). The same occurred with a TLG cut-off of 36.17 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001, <span class="elsevierStyleItalic">χ</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>6.45) and a TLG cut-off of 59.05 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.043, <span class="elsevierStyleItalic">χ</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>4.09) for OS (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>). On the other hand, no associations were found with the DFS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.119 and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.197 respectively using the previously given cut-off values). No SUV-related variable was associated to OS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.384 for SUVmax, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.351 for SUVpeak and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.299 for SUVmean) or DFS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.138 for SUVmax, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.113 for SUVpeak and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.307 for SUVmean).</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0175" class="elsevierStylePara elsevierViewall">In multivariate analysis only MTV showed association with OS (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.009, HR<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>1.027, 95% CI<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>1.007–1.048). This suggests that a one-unit increase in MTV could correspond to a significant increase of 2.7% in the risk of death. Furthermore, the <span class="elsevierStyleSup">18</span>F-FDG-PET/CT stage showed a relation that was close to significance (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.068, HR<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>4.372, 95% IC<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.899–21.266). Thus, stage IV seems to show an increased risk of death (approximately four times higher) regarding the rest of the metabolic stages. With respect to DFS, only the <span class="elsevierStyleSup">18</span>F-FDG-PET/CT stage had a significant association (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001, HR<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>9.865, 95% CI<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>3.087–31.528), with a ninefold increased risk of recurrence when compared to the other stages.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Discussion</span><p id="par0180" class="elsevierStylePara elsevierViewall">Relations between biology and basal tumor SUVmax obtained by <span class="elsevierStyleSup">18</span>F-FDG PET/CT have been previously described.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">16</span></a> Moreover, molecular biological characteristics of the tumor are important determinants of the final histopathological response after NC. These connections can explain the association found in some studies, reporting higher values of baseline glycolytic metabolism in responder breast tumors.<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">8,9,17</span></a> However, this association is controversial as some authors have not referenced such correlation <a class="elsevierStyleCrossRefs" href="#bib0240"><span class="elsevierStyleSup">18–21</span></a> and others have even found that <span class="elsevierStyleSup">18</span>F-FDG tumor avidity was associated with poorer response to NC.<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">22</span></a></p><p id="par0185" class="elsevierStylePara elsevierViewall">Furthermore, among the studies that describe association of basal tumor metabolism with NC response, the reference values obtained for SUVmax were variable, ranging from 7.4 to 15.9.<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">8,9,17</span></a> Based on the lack of reproducibility of SUV variables, in recent years, more integrated metabolic tumor metrics have been developed, such as volume-based variables. These metabolic tumor burden measurements, including MTV and TLG, incorporate both metabolic activity and tumor volume.</p><p id="par0190" class="elsevierStylePara elsevierViewall">Previous authors have addressed the utility of volume-based metabolic parameters after NC in the prediction of response, stating that MTV and TLG could be more robust indices for discriminating pathological responders compared to SUVmax.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">23</span></a> However, no previous reported study has analyzed the relations between volume-based metabolic parameters on basal <span class="elsevierStyleSup">18</span>F-FDG PET/CT and NC response. In the present study, associations between SUV and a volume-based variable, as TLG, with NC response were found. Paradoxically, tumors with higher basal FDG activity and metabolic tumor burden (combination of MTV and SUVmean) were associated to a better NC response. The tumor biology could be responsible for these results, as more aggressive breast tumors are associated with a higher metabolism, tumor burden and also a better histological response to NC.<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">1,10</span></a></p><p id="par0195" class="elsevierStylePara elsevierViewall">Regarding the relations between <span class="elsevierStyleSup">18</span>F-FDG tumor uptake and prognosis, previous literature has found different results, probably explained by differences in the tumor characteristics and methodology.<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">2–7</span></a> In our previous experiment, SUVmax had a limited prognostic performance, with a sensitivity and specificity of 64% and 57% respectively for a cut-off value of SUVmax in breast tumor of 6.05.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">4</span></a> Thus SUVmax does not seem to be a strong variable to predict disease evolution.</p><p id="par0200" class="elsevierStylePara elsevierViewall">Since the SUV and volume-based variables are continuous, which makes searching for significant relationships with prognosis rather difficult in the univariate analysis, we decided to convert them into categorical variables using ROC analysis.</p><p id="par0205" class="elsevierStylePara elsevierViewall">With respect to volume-based variables obtained on baseline <span class="elsevierStyleSup">18</span>F-FDG PET/CT, their prognostic aim has not been fully assessed. Kim et al.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">11</span></a> found in 53 patients with operable BC that high MTV of breast tumor was associated with shorter DFS and OS in univariate analysis although these failed to be statistically significant prognostic factors in multivariate analysis. Nakajima et al.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">12</span></a> found in a group of patients with 1–3 positive nodes that the addition of MTV to ER or triple negative status had potential benefits for identifying a subgroup at higher risk for recurrence. However, these studies were focused on operable BC. The present study has found relations between MTV with OS and DFS and between TLG with OS including patients with breast cancer with NC indication. However, no associations with SUV-based variables were obtained. These results give a more relevant prognostic value for metabolic tumor burden comparing with an isolated SUV measure, as SUVmax and SUVpeak, which only represents the more hypermetabolic tumor portion but not its entire metabolic extent. Moreover, MTV kept its significant association with OS in multivariate analysis but failed in the prediction of DFS against the metabolic stage. Thus the MTV in breast tumor seems to lose its prognostic value (time to recurrence) in IV stage patients, which means that probably a more integrated metabolic tumor burden assessment, including all the pathologic lesions, should be taken into consideration in cases with a suggestion of distant metastases on the <span class="elsevierStyleSup">18</span>F-FDG PET/CT scan.</p><p id="par0210" class="elsevierStylePara elsevierViewall">Yue et al.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">24</span></a> found that patients with large metabolic volume had worse outcomes than those with small volume in a group of triple negative BC considering a cut-off SUV mean of 2.9.</p><p id="par0215" class="elsevierStylePara elsevierViewall">In a study including stage III and IV BC, Chen et al.<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">25</span></a> used the TLG to establish a risk classification (high risk patients for TLG30%<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>158<span class="elsevierStyleHsp" style=""></span>g). In their results, the TLG30% from pre-treatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT was found to correlate independently with survival outcomes and showed utility to stratify both patients with stage III and those with stage IV BC. In the present study, the MTV lost its prognostic capacity when the <span class="elsevierStyleSup">18</span>F-FDG PET/CT stage was taken into account in the multivariate analysis.</p><p id="par0220" class="elsevierStylePara elsevierViewall">From our results, no association of breast NC response with OS or DFS was found. Breast NC response is not considered a single end-point in the prognosis of these patients in the same way as lymph node response.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">26</span></a> However, lymph node histological response assessment was not included as a variable because only metabolic expression and NC response in primary breast tumor were considered. On the other hand, metabolic stage seems to be the main prognostic factor in our analysis.</p><p id="par0225" class="elsevierStylePara elsevierViewall">Other authors explored a more global variable of tumor burden, adding lymph node or distant metastases information. Hyun et al.<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">27</span></a> found that MTV total (breast lesion plus lymph nodes) after NC had the highest association with outcome compared with tumor subtype and pathologic tumor response. On the other hand, other authors have not found this association.<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">28</span></a> Son et al.,<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">29</span></a> analyzing the whole-body MTV value in patients with distant metastases, found that this variable was an independent prognostic factor for OS. With respect to the TLG, some authors state that it may be a more informative biomarker of OS than SUVmax for patients with lymph nodes and liver metastases.<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">30</span></a></p><p id="par0230" class="elsevierStylePara elsevierViewall">Concerning the limitations, OS is not a reliable endpoint for assessing outcome in oncological processes with low mortality such as BC with NC indication. Thus, the follow-up duration in the study population was perhaps not enough to establish the OS analysis. Moreover, the tumor characteristics were heterogeneous, and could limit the results obtained. On the other hand, although some selection bias could have been introduced, as only tumors of at least 2<span class="elsevierStyleHsp" style=""></span>cm in diameter and visually detectable by <span class="elsevierStyleSup">18</span>F-FDG PET/CT were chosen for automatic segmentation, the selection of tumors with a size higher than twice the spatial PET resolution is mandatory to minimize the partial volume effect.</p><p id="par0235" class="elsevierStylePara elsevierViewall">With respect to the strengths, this is the first reported evidence that shows the significant association of volume-based variables with DFs, OS and DSF, with respect to SUV-variables. Thus, parameters more representative of metabolic tumor burden seem more robust in predicting the prognosis than SUV-based variables.</p><p id="par0240" class="elsevierStylePara elsevierViewall">Regarding the applicability of the obtained results, patients with larger <span class="elsevierStyleSup">18</span>F-FDG avid-volume on PET should undergo a closer follow up based on their higher probability of recurrence and death comparing to the rest of patients. On the other hand, the metabolic prediction of NC response in breast tumors seem to have a weaker value, explained by the absence of prognostic power probably due to the concurrence of stronger predictors of outcome, as metabolic information, in our analysis.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conclusion</span><p id="par0245" class="elsevierStylePara elsevierViewall">Volume-based metabolic variables obtained with <span class="elsevierStyleSup">18</span>F-FDG PET/CT, unlike SUV based variables, were good predictors of both neoadjuvant chemotherapy response and prognosis in patients with locally advanced breast cancer.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Ethical disclosures</span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Research involving human participants and/or animals</span><p id="par0250" class="elsevierStylePara elsevierViewall">All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Informed consent</span><p id="par0255" class="elsevierStylePara elsevierViewall">Informed consent was obtained from all individual participants included in the study.</p></span></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conflict of interests</span><p id="par0260" class="elsevierStylePara elsevierViewall">The authors declare that they have no conflict of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres993901" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Aim" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec957286" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres993902" "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" => "Conclusión" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec957285" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Patients" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Pre-treatment and post-treatment histopathological analysis" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "F-FDG-PET/CT imaging acquisition and interpretation" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Lesion segmentation and analysis" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0040" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0045" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0050" "titulo" => "Conclusion" ] 9 => array:3 [ "identificador" => "sec0055" "titulo" => "Ethical disclosures" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0060" "titulo" => "Research involving human participants and/or animals" ] 1 => array:2 [ "identificador" => "sec0065" "titulo" => "Informed consent" ] ] ] 10 => array:2 [ "identificador" => "sec0070" "titulo" => "Conflict of interests" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2017-06-22" "fechaAceptado" => "2017-09-13" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec957286" "palabras" => array:5 [ 0 => "<span class="elsevierStyleSup">18</span>F-FDG PET/CT" 1 => "Breast cancer" 2 => "Volume-based metabolic variables" 3 => "Prognosis" 4 => "Neoadjuvant chemotherapy response" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec957285" "palabras" => array:5 [ 0 => "<span class="elsevierStyleSup">18</span>F-FDG PET/TC" 1 => "Cáncer de mama" 2 => "Variables volumétricas metabólicas" 3 => "Pronóstico" 4 => "Respuesta a quimioterapia neoadyuvante" ] ] ] ] "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 investigate the usefulness of metabolic variables using <span class="elsevierStyleSup">18</span>F-FDG PET/CT in the prediction of neoadjuvant chemotherapy (NC) response and the prognosis in locally advanced breast cancer (LABC).</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Prospective study including 67 patients with LABC, NC indication and a baseline <span class="elsevierStyleSup">18</span>F-FDG PET/CT. After breast tumor segmentation, SUV variables (SUVmax, SUVmean and SUVpeak) and volume-based variables, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were obtained. Tumors were grouped into molecular phenotypes, and classified as responders or non-responders after completion of NC. Disease-free status (DFs), disease-free survival (DFS), and overall survival (OS) were assessed. A univariate and multivariate analysis was performed to study the potential of all variables to predict DFs, DFS, and OS.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Fourteen patients were classified as responders. Median<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD of DFS and OS was 43<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15 and 46<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13 months, respectively. SUV and TLG showed a significant correlation (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.005) with the histological response, with higher values in responders compared to non-responders. MTV and TLG showed a significant association with DFs (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.015 and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.038 respectively). Median, mean and SD of MTV and TLG for patients with DFs were: 8.90, 13.73, 15.10 and 33.78, and 90.54 and 144.64, respectively. Median, mean and SD of MTV and TLG for patients with non-DFs were: 16.72, 29.70 and 31.09 and 90.89, 210.98 and 382.80, respectively. No significant relationships were observed with SUV variables and DFs. Volume-based variables were significantly associated with OS and DFS, although in multivariate analysis only MTV was related to OS. No SUV variables showed an association with the prognosis.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Volume-based metabolic variables obtained with <span class="elsevierStyleSup">18</span>F-FDG PET/CT, unlike SUV based variables, were good predictors of both neoadjuvant chemotherapy response and prognosis.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Aim" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] "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">Investigar la utilidad de las variables metabólicas obtenidas en la <span class="elsevierStyleSup">18</span>F-FDG PET/TC en la predicción de la respuesta a quimioterapia neoadyuvante (QN) y el pronóstico en cáncer de mama locamente 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">Estudio prospectivo que incluye 67 pacientes con CMLA, indicación de QN y <span class="elsevierStyleSup">18</span>F-FDG PET/TC basal. Se obtuvieron variables SUV (SUVmax, SUVmedio y SUVpico) y volumétricas, tales como el volumen tumoral metabólico (VTM) y la glicólisis total lesional (GTL). Los tumores se agruparon en fenotipos moleculares y fueron clasificadas como respondedores y no respondedores tras la finalización de la QN. Se obtuvo el estado libre de enfermedad (ELE), supervivencia libre de enfermedad (SLE) y supervivencia global (SG). Se realizó análisis univariante y multivariante para estudiar el potencial de todas las variables en la predicción de la ELE, SLE y SG.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Catorce pacientes se clasificaron como respondedoras. La media ±<span class="elsevierStyleHsp" style=""></span>DE de la SLE y la SG fue de 43<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15 y 46<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13 meses, respectivamente. El SUV y la GTL mostraron una relación significativa (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0,005) con la respuesta histológica, con mayores valores en las pacientes respondedoras con respecto a las no-respondedoras. El VTM y la GTL mostraron asociación con el ELE (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,015 y p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,038, respectivamente). La mediana, media y DS del VTM y la GTL para pacientes en ELE fue de 8,90, 13,73, 15,10 y del 33,78, 90,54 y 144,64, respectivamente. La mediana, media y DS del VTM y la GTL para pacientes en no ELE fueron de: 16,72, 29,70 y 31,09 y de 90,89, 210,98 y 382,80, respectivamente. No se encontró relación con las variables de SUV y el ELE. Las variables volumétricas se asociaron de forma significativa con la SG y con la SLE, aunque en el análisis multivariante solo el VTM se relacionó con la SG. Ninguna variable de SUV mostró asociación con el pronóstico.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusión</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Las variables metabólicas obtenidas con la <span class="elsevierStyleSup">18</span>F-FDG PET/TC, de forma distinta a las variables de SUV, fueron buenos predictores tanto de la respuesta al tratamiento quimioterápico neoadyuvante y el pronóstico.</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" => "Conclusión" ] ] ] ] "NotaPie" => array:1 [ 0 => array:3 [ "etiqueta" => "1" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">C/ Obispo Rafael Torija s/n, University General Hospital, 13005 Ciudad Real, Spain.</p>" "identificador" => "fn0005" ] ] "multimedia" => array:6 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1617 "Ancho" => 3084 "Tamanyo" => 263673 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">52-year-old female with triple negative invasive ductal carcinoma, stage T2NxM1 by <span class="elsevierStyleSup">18</span>F-FDG PET/CT. (A) Maximum intensity projection of a <span class="elsevierStyleSup">18</span>F-FDG-PET/CT image that represents a left breast tumor and multiple physiological brown fat deposits in the cervical and paraspinal regions. (B) Axial <span class="elsevierStyleSup">18</span>F-FDG-PET/CT, (C) CT and (D) co-registration of these that show a right lung nodule with FDG avidity. (E) Breast tumor in <span class="elsevierStyleSup">18</span>F-FDG-PET/CT and CT co-registered axial projection. (F) Breast tumor voxels after 3D segmentation using a code developed and implemented in-house at MATLAB and (G) lesion reconstruction. After neoadjuvant chemotherapy no complete histological response of breast tumor was achieved although the pulmonary nodule disappeared. Patient was in DFs during follow-up (OS and DFS of 40 months).</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" => 1058 "Ancho" => 2497 "Tamanyo" => 102308 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier analysis using a MTV cut-off of 14.33 shows significant association with OS (A) and DFS (B). Cut-off details are displayed in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>.</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" => 1047 "Ancho" => 2520 "Tamanyo" => 102229 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier analysis using a TLG cut-off of 59.05 (A) and 36.17 (B) shows significant association with OS. Cut-off details are displayed in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">NC, neoadjuvant chemotherapy; n.a, not assessed.</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="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Characteristics \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">% \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">Histology</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>IDC \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">95.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>ILC \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">Risk phenotype</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Low \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Intermediate \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>High \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">NC breast response</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20.9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">61.1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>n.a. \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">Metabolic stage</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>II or III \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76.1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>IV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>n.a. \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">Disease free status</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">54 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1685152.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Patient's characteristics.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">NC, neoadjuvant chemotherapy; SUV, standardized uptake value; 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="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">SUVmax</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">SUV peak</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">SUV mean</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">MTV</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">TLG</th></tr><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Value \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Value \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Value \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Value \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Value \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="12" align="left" valign="top"><span class="elsevierStyleItalic">NC response</span></td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.77 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">96.39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">258.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">(<span class="elsevierStyleItalic">n</span><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="table-entry " align="left" valign="top">SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">34.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.423 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">384.63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.021 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">33.78 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.77 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">(<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>41) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.08 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">97.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="12" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="12" align="left" valign="top"><span class="elsevierStyleItalic">DFs</span></td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.43 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">33.78 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.93 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.54 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">90.54 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">(<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>54) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.32 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.711 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.31 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.611 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.830 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.015 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">144.64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.038 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.22 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.57 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">90.89 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.70 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">210.98 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">(<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.98 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">31.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">382.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1685151.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Relation between SUV and volume-based variables with NC response and DFs.</p>" ] ] 5 => 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="spar0085" class="elsevierStyleSimplePara elsevierViewall">AUC, area under curve; DFs, disease free status; CI, confidence interval; Se, sensitivity; Sp, specificity; NC, neoadjuvant chemotherapy; SUV, standardized uptake value; 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="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">AUC \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Cut-off \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Se (%) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Sp (%) \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="7" align="left" valign="top"><span class="elsevierStyleItalic">DFs</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>MTV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.739 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.013 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.585–0.892 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>TLG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.705 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.033 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.548–0.861 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">64 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="7" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="7" align="left" valign="top"><span class="elsevierStyleItalic">NC response</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>SUVmax \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.787 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.657–0.917 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.93 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>SUVpeak \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.790 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.656–0.924 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.96 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">67 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>SUVmean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.790 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.664–0.916 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">65 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>TLG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.704 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.021 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.546–0.862 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">59.05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">61 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1685150.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Results of ROC analysis of SUV and volume-based metabolic variables in the prediction of DFs and NC response (only significant associations are showed).</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:30 [ 0 => array:3 [ "identificador" => "bib0155" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "P. Cortazar" 1 => "L. Zhang" 2 => "M. Untch" 3 => "K. Mehta" 4 => "J.P. Costantino" 5 => "N. Wolmark" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/S0140-6736(13)62422-8" "Revista" => array:6 [ "tituloSerie" => "Lancet" "fecha" => "2014" "volumen" => "384" "paginaInicial" => "164" "paginaFinal" => "172" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24529560" "web" => "Medline" ] ] ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0160" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predicting the prognoses of breast carcinoma patients with positron emission tomography using 2-deoxy-2-fluoro[18F]-<span class="elsevierStyleSmallCaps">d</span>-glucose" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "M. Oshida" 1 => "K. Uno" 2 => "SuzukiM" 3 => "T. Nagashima" 4 => "H. Hashimoto" 5 => "H. Yagata" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Cancer" "fecha" => "1998" "volumen" => "82" "paginaInicial" => "2227" "paginaFinal" => "2234" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/9610703" "web" => "Medline" ] ] ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0165" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18F-FDG uptake by metastatic axillary lymph nodes on pretreatment PET/CT as a prognostic factor for recurrence in patients with invasive ductal breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "B.-I. Song" 1 => "S.-W. Lee" 2 => "S.Y. Jeong" 3 => "Y.S. Chae" 4 => "W.K. Lee" 5 => "B.C. Ahn" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.2967/jnumed.111.098640" "Revista" => array:6 [ "tituloSerie" => "J Nucl Med" "fecha" => "2012" "volumen" => "53" "paginaInicial" => "1337" "paginaFinal" => "1344" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/22870824" "web" => "Medline" ] ] ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0170" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Basal <span class="elsevierStyleSup">18</span>F-FDG PET/CT as a prognostic biomarker in patients with locally advanced breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A.M. García Vicente" 1 => "A. Soriano Castrejón" 2 => "J.F. López-Fidalgo" 3 => "M. Amo-Salas" 4 => "M.M. Muñoz Sánchez" 5 => "R. Álvarez Cabellos" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Eur J Nucl Med Molec Imaging" "fecha" => "2015" "volumen" => "42" "paginaInicial" => "1804" "paginaFinal" => "1813" ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0175" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The prognostic value of 18F-FDG PET/CT for early recurrence in operable breast cancer: comparison with TNM Stage" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "O. Joo Hyun" 1 => "W.H. Choi" 2 => "E.J. Han" 3 => "E.-K. Choi" 4 => "B.J. Chae" 5 => "Y.-G. Park" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s13139-013-0232-6" "Revista" => array:6 [ "tituloSerie" => "Nucl Med Mol Imaging" "fecha" => "2013" "volumen" => "47" "paginaInicial" => "263" "paginaFinal" => "267" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24900122" "web" => "Medline" ] ] ] ] ] ] ] ] 5 => array:3 [ "identificador" => "bib0180" "etiqueta" => "6" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Tumor metabolism and blood flow changes by positron emission tomography: relation to survival in patients treated with neoadjuvant chemotherapy for locally advanced breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "L.K. Dunnwald" 1 => "J.R. Gralow" 2 => "G.K. Ellis" 3 => "R.B. Livingston" 4 => "H.M. Linden" 5 => "J.M. Specht" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1200/JCO.2007.15.4385" "Revista" => array:6 [ "tituloSerie" => "J Clin Oncol" "fecha" => "2008" "volumen" => "26" "paginaInicial" => "4449" "paginaFinal" => "4457" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/18626006" "web" => "Medline" ] ] ] ] ] ] ] ] 6 => array:3 [ "identificador" => "bib0185" "etiqueta" => "7" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Preoperative evaluation of prognosis in breast cancer patients by [(18)F]2-deoxy-2-fluoro-D-glucose-positron emission tomography" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "T. Inoue" 1 => "K. Yutani" 2 => "T. Taguchi" 3 => "Y. Tamaki" 4 => "E. Shiba" 5 => "S. Noguchi" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00432-003-0536-5" "Revista" => array:6 [ "tituloSerie" => "J Cancer Res Clin Oncol" "fecha" => "2004" "volumen" => "130" "paginaInicial" => "273" "paginaFinal" => "278" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/14986112" "web" => "Medline" ] ] ] ] ] ] ] ] 7 => array:3 [ "identificador" => "bib0190" "etiqueta" => "8" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Monitoring primary systemic therapy of large and locally advanced breast cancer by using sequential positron emission tomography imaging with [18F]fluorodeoxyglucose" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. Schwarz-Dose" 1 => "M. Untch" 2 => "R. Tiling" 3 => "S. Sassen" 4 => "S. Mahner" 5 => "S. Kahlert" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1200/JCO.2008.17.2650" "Revista" => array:6 [ "tituloSerie" => "J Clin Oncol" "fecha" => "2009" "volumen" => "27" "paginaInicial" => "535" "paginaFinal" => "541" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/19075273" "web" => "Medline" ] ] ] ] ] ] ] ] 8 => array:3 [ "identificador" => "bib0195" "etiqueta" => "9" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "FDG-PET/CT for the early prediction of histopathological complete response to neoadjuvant chemotherapy in breast cancer patients: initial results" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "C. Buchbender" 1 => "S. Kuemmel" 2 => "O. Hoffmann" 3 => "A.R. Stahl" 4 => "R. Kimmig" 5 => "F. Otterbach" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1258/ar.2012.110699" "Revista" => array:6 [ "tituloSerie" => "Acta Radiol" "fecha" => "2012" "volumen" => "53" "paginaInicial" => "628" "paginaFinal" => "636" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/22761341" "web" => "Medline" ] ] ] ] ] ] ] ] 9 => array:3 [ "identificador" => "bib0200" "etiqueta" => "10" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Glycolitic activity with <span class="elsevierStyleSup">18</span>F-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 => "M.A. Cruz Mora" 2 => "A.A. León Martín" 3 => "M.M. Muñoz Sánchez" 4 => "F. Relea Calatayud" 5 => "O.V. Gómez López" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Tumor Biol" "fecha" => "2014" "volumen" => "35" "paginaInicial" => "11613" "paginaFinal" => "11620" ] ] ] ] ] ] 10 => array:3 [ "identificador" => "bib0205" "etiqueta" => "11" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Prognostic significance of metabolic tumor volume measured by (18)F-FDG PET/CT in operable primary breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. Kim" 1 => "S.W. Yoo" 2 => "S.R. Kang" 3 => "S.G. Cho" 4 => "J.R. Oh" 5 => "A. Chong" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s13139-012-0161-9" "Revista" => array:6 [ "tituloSerie" => "Nucl Med Mol Imaging" "fecha" => "2012" "volumen" => "46" "paginaInicial" => "278" "paginaFinal" => "285" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24900075" "web" => "Medline" ] ] ] ] ] ] ] ] 11 => array:3 [ "identificador" => "bib0210" "etiqueta" => "12" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Volume-based parameters of 18F-fluorodeoxyglucose positron emission tomography/computed tomography improve disease recurrence prediction in postmastectomy breast cancer patients with 1 to 3 positive axillary lymph nodes" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "N. Nakajima" 1 => "M. Kataoka" 2 => "Y. Sugawara" 3 => "T. Ochi" 4 => "S. Kiyoto" 5 => "S. Ohsumi" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.ijrobp.2013.08.001" "Revista" => array:6 [ "tituloSerie" => "Int J Radiat Oncol Biol Phys" "fecha" => "2013" "volumen" => "87" "paginaInicial" => "738" "paginaFinal" => "746" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24064322" "web" => "Medline" ] ] ] ] ] ] ] ] 12 => array:3 [ "identificador" => "bib0215" "etiqueta" => "13" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Early and delayed prediction of axillary lymph node neoadjuvant response by 18F-FDG PET/CT in patients with locally advanced breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A.M. García Vicente" 1 => "A. Soriano Castrejón" 2 => "A. León Martín" 3 => "F. Relea Calatayud" 4 => "M.M. Muñoz Sánchez" 5 => "M.A. Cruz Mora" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-013-2657-7" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2014" "volumen" => "41" "paginaInicial" => "1309" "paginaFinal" => "1318" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24744045" "web" => "Medline" ] ] ] ] ] ] ] ] 13 => array:3 [ "identificador" => "bib0220" "etiqueta" => "14" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Goldhirsch" 1 => "W.C. Wood" 2 => "A.S. Coates" 3 => "R.D. Gelber" 4 => "B. Thürlimann" 5 => "H.J. Senn" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1093/annonc/mdr304" "Revista" => array:6 [ "tituloSerie" => "Ann Oncol" "fecha" => "2011" "volumen" => "22" "paginaInicial" => "1736" "paginaFinal" => "1747" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/21709140" "web" => "Medline" ] ] ] ] ] ] ] ] 14 => array:3 [ "identificador" => "bib0225" "etiqueta" => "15" "referencia" => array:1 [ 0 => array:1 [ "referenciaCompleta" => "<a id="intr0005" class="elsevierStyleInterRef" href="http://www.nlm.nih.gov/bsd/uniform_requirements.html">http://www.nlm.nih.gov/bsd/uniform_requirements.html</a>." ] ] ] 15 => array:3 [ "identificador" => "bib0230" "etiqueta" => "16" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18F-FDG uptake in breast cancer correlates with immunohistochemically defined subtypes" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "H.R. Koo" 1 => "J.S. Park" 2 => "K.W. Kang" 3 => "N. Cho" 4 => "J.M. Chang" 5 => "M.S. Bae" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00330-013-3037-1" "Revista" => array:6 [ "tituloSerie" => "Eur Radiol" "fecha" => "2014" "volumen" => "24" "paginaInicial" => "610" "paginaFinal" => "618" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24097303" "web" => "Medline" ] ] ] ] ] ] ] ] 16 => array:3 [ "identificador" => "bib0235" "etiqueta" => "17" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18F-Fluorodeoxyglucose positron emission tomography optimizes neoadjuvant chemotherapy for primary breast cancer to achieve pathological complete response" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S. Ueda" 1 => "T. Saeki" 2 => "T. Shigekawa" 3 => "J. Omata" 4 => "T. Moriya" 5 => "J. Yamamoto" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s10147-011-0287-2" "Revista" => array:6 [ "tituloSerie" => "Int J Clin Oncol" "fecha" => "2012" "volumen" => "17" "paginaInicial" => "276" "paginaFinal" => "282" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/21830087" "web" => "Medline" ] ] ] ] ] ] ] ] 17 => array:3 [ "identificador" => "bib0240" "etiqueta" => "18" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "FDG PET/CT during neoadjuvant chemotherapy may predict response in ER-positive/HER2-negative and triple negative, but not in HER2-positive breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "B.B. Koolen" 1 => "K.E. Pengel" 2 => "J. Wesseling" 3 => "W.V. Vogel" 4 => "M.J. Vrancken Peeters" 5 => "A.D. Vincent" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.breast.2012.12.020" "Revista" => array:6 [ "tituloSerie" => "Breast" "fecha" => "2013" "volumen" => "22" "paginaInicial" => "691" "paginaFinal" => "697" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/23414930" "web" => "Medline" ] ] ] ] ] ] ] ] 18 => array:3 [ "identificador" => "bib0245" "etiqueta" => "19" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predictive value of PET-CT for pathological response in stages II and III breast cancer patients following neoadjuvant chemotherapy with docetaxel" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:5 [ 0 => "M.A. García García-Esquinas" 1 => "J. Arrazola García" 2 => "J.A. García-Sáenz" 3 => "V. Furió-Bacete" 4 => "M.E. Fuentes Ferrer" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.remn.2013.04.008" "Revista" => array:6 [ "tituloSerie" => "Rev Esp Med Nucl Imagen Mol" "fecha" => "2014" "volumen" => "33" "paginaInicial" => "14" "paginaFinal" => "21" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/23809513" "web" => "Medline" ] ] ] ] ] ] ] ] 19 => array:3 [ "identificador" => "bib0250" "etiqueta" => "20" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predictive value of neoadjuvant chemotherapy failure in breast cancer using FDG-PET after the first course" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "H. Kolesnikov-Gauthier" 1 => "L. Vanlemmens" 2 => "M.-C. Baranzelli" 3 => "P. Vennin" 4 => "V. Servent" 5 => "C. Fournier" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s10549-011-1832-4" "Revista" => array:6 [ "tituloSerie" => "Breast Cancer Res Treat" "fecha" => "2012" "volumen" => "131" "paginaInicial" => "517" "paginaFinal" => "525" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/22037787" "web" => "Medline" ] ] ] ] ] ] ] ] 20 => array:3 [ "identificador" => "bib0255" "etiqueta" => "21" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18F-FDG PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Berriolo-Riedinger" 1 => "C. Touzery" 2 => "J.M. Riedinger" 3 => "M. Toubeau" 4 => "B. Coudert" 5 => "L. Arnould" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s00259-007-0459-5" "Revista" => array:6 [ "tituloSerie" => "Eur J Nucl Med Mol Imaging" "fecha" => "2007" "volumen" => "34" "paginaInicial" => "1915" "paginaFinal" => "1924" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/17579854" "web" => "Medline" ] ] ] ] ] ] ] ] 21 => array:3 [ "identificador" => "bib0260" "etiqueta" => "22" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Changes in blood flow and metabolism in locally advanced breast cancer treated with neoadjuvant chemotherapy" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D.A. Mankoff" 1 => "L.K. Dunnwald" 2 => "J.R. Gralow" 3 => "G.K. Ellis" 4 => "E.K. Schubert" 5 => "J. Tseng" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "J Nucl Med" "fecha" => "2003" "volumen" => "44" "paginaInicial" => "1806" "paginaFinal" => "1814" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/14602864" "web" => "Medline" ] ] ] ] ] ] ] ] 22 => array:3 [ "identificador" => "bib0265" "etiqueta" => "23" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Usefulness of combined metabolic-volumetric indices of (18)F-FDG PET/CT for the early prediction of neoadjuvant chemotherapy outcomes in breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "H.J. Im" 1 => "Y.K. Kim" 2 => "Y.I. Kim" 3 => "J.J. Lee" 4 => "W.W. Lee" 5 => "S.E. Kim" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s13139-012-0181-5" "Revista" => array:6 [ "tituloSerie" => "Nucl Med Mol Imaging" "fecha" => "2013" "volumen" => "47" "paginaInicial" => "36" "paginaFinal" => "43" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24895506" "web" => "Medline" ] ] ] ] ] ] ] ] 23 => array:3 [ "identificador" => "bib0270" "etiqueta" => "24" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Stratifying triple-negative breast cancer prognosis using 18F-FDG-PET/CT imaging" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "Y. Yue" 1 => "X. Cui" 2 => "S. Bose" 3 => "W. Audeh" 4 => "X. Zhang" 5 => "B. Fraass" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s10549-015-3558-1" "Revista" => array:6 [ "tituloSerie" => "Breast Cancer Res Treat" "fecha" => "2015" "volumen" => "153" "paginaInicial" => "607" "paginaFinal" => "616" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26346756" "web" => "Medline" ] ] ] ] ] ] ] ] 24 => array:3 [ "identificador" => "bib0275" "etiqueta" => "25" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Risk stratification in patients with advanced-stage breast cancer by pretreatment [(18) F]FDG PET/CT" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "S. Chen" 1 => "N.K. Ibrahim" 2 => "Y. Yan" 3 => "S.T. Wong" 4 => "H. Wang" 5 => "F.C. Wong" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/cncr.29565" "Revista" => array:6 [ "tituloSerie" => "Cancer" "fecha" => "2015" "volumen" => "121" "paginaInicial" => "3965" "paginaFinal" => "3974" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26249241" "web" => "Medline" ] ] ] ] ] ] ] ] 25 => array:3 [ "identificador" => "bib0280" "etiqueta" => "26" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "G. Von Minckwitz" 1 => "M. Untch" 2 => "J.U. Blohmer" 3 => "S.D. Costa" 4 => "H. Eidtmann" 5 => "P.A. Fasching" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1200/JCO.2011.38.8595" "Revista" => array:6 [ "tituloSerie" => "J Clin Oncol" "fecha" => "2012" "volumen" => "30" "paginaInicial" => "1796" "paginaFinal" => "1804" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/22508812" "web" => "Medline" ] ] ] ] ] ] ] ] 26 => array:3 [ "identificador" => "bib0285" "etiqueta" => "27" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Volume-based metabolic tumor response to neoadjuvant chemotherapy is associated with an increased risk of recurrence in breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S.H. Hyun" 1 => "H.K. Ahn" 2 => "Y.H. Park" 3 => "Y.H. Im" 4 => "W.H. Kil" 5 => "J.E. Lee" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1148/radiol.14141129" "Revista" => array:6 [ "tituloSerie" => "Radiology" "fecha" => "2015" "volumen" => "275" "paginaInicial" => "235" "paginaFinal" => "244" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/25496075" "web" => "Medline" ] ] ] ] ] ] ] ] 27 => array:3 [ "identificador" => "bib0290" "etiqueta" => "28" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "18F-FDG PET/Ct to predict response to neoadjuvant chemotherapy and prognosis in inflammatory breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "L. Champion" 1 => "F. Lerebours" 2 => "J.L. Alberini" 3 => "E. Fourme" 4 => "E. Gontier" 5 => "F. Bertrand" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.2967/jnumed.115.158287" "Revista" => array:6 [ "tituloSerie" => "J Nucl Med" "fecha" => "2015" "volumen" => "56" "paginaInicial" => "1315" "paginaFinal" => "1321" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26159587" "web" => "Medline" ] ] ] ] ] ] ] ] 28 => array:3 [ "identificador" => "bib0295" "etiqueta" => "29" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Whole-body metabolic tumor volume, as determined by (18)F-FDG PET/CT, as a prognostic factor of outcome for patients with breast cancer who have distant metastasis" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "S.H. Son" 1 => "S.W. Lee" 2 => "S.Y. Jeong" 3 => "B.I. Song" 4 => "Y.S. Chae" 5 => "B.C. Ahn" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.2214/AJR.14.13906" "Revista" => array:6 [ "tituloSerie" => "AJR Am J Roentgenol" "fecha" => "2015" "volumen" => "205" "paginaInicial" => "878" "paginaFinal" => "885" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26204115" "web" => "Medline" ] ] ] ] ] ] ] ] 29 => array:3 [ "identificador" => "bib0300" "etiqueta" => "30" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Prognostic value of quantitative fluorodeoxyglucose measurements in newly diagnosed metastatic breast cancer" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "G.A. Ulaner" 1 => "A. Eaton" 2 => "P.G. Morris" 3 => "J. Lilienstein" 4 => "K. Jhaveri" 5 => "S. Patil" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1002/cam4.119" "Revista" => array:6 [ "tituloSerie" => "Cancer Med" "fecha" => "2013" "volumen" => "2" "paginaInicial" => "725" "paginaFinal" => "733" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/24403238" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] ] "idiomaDefecto" => "en" "url" => "/22538089/0000003700000002/v1_201803020417/S2253808917302070/v1_201803020417/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/22538089/0000003700000002/v1_201803020417/S2253808917302070/v1_201803020417/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808917302070?idApp=UINPBA00004N" ]
