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array:24 [ "pii" => "S2253808923000010" "issn" => "22538089" "doi" => "10.1016/j.remnie.2023.01.001" "estado" => "S300" "fechaPublicacion" => "2023-07-01" "aid" => "1418" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "copyrightAnyo" => "2023" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2023;42:223-30" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "Traduccion" => array:1 [ "es" => array:19 [ "pii" => "S2253654X2300001X" "issn" => "2253654X" "doi" => "10.1016/j.remn.2023.01.001" "estado" => "S300" "fechaPublicacion" => "2023-07-01" "aid" => "1418" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2023;42:223-30" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Heterogeneidad del tumor primario en la<span class="elsevierStyleSup">18</span>F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras terapia neoadyuvante" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "223" "paginaFinal" => "230" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Primary tumor heterogeneity on pretreatment<span class="elsevierStyleSup">18</span>F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy" ] ] "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" => 1755 "Ancho" => 3341 "Tamanyo" => 543989 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Análisis de textura de imágenes PET-FDG utilizando el <span class="elsevierStyleItalic">software</span> LİFEx<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">14</span></a>. Las imágenes de los pacientes se cargaron en formato DİCOM. Los volúmenes de interés (VOİ) de los tumores rectales primarios se dibujaron de forma semiautomática mediante el uso de la herramienta VOİ tridimensional (3D) (cuadro púrpura en el panel derecho). Las lesiones tumorales primarias se segmentaron utilizando el 40% del valor máximo en el VOI como umbral (recuadro azul en el panel derecho). Las características de textura se extrajeron de los VOI tumorales utilizando la sección de extracción de características de textura (cuadro amarillo en el panel superior).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "S. Gülbahar Ateş, G. Bilir Dilek, G. Uçmak" "autores" => array:3 [ 0 => array:2 [ "nombre" => "S." "apellidos" => "Gülbahar Ateş" ] 1 => array:2 [ "nombre" => "G." "apellidos" => "Bilir Dilek" ] 2 => array:2 [ "nombre" => "G." "apellidos" => "Uçmak" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2253808923000010" "doi" => "10.1016/j.remnie.2023.01.001" "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/S2253808923000010?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X2300001X?idApp=UINPBA00004N" "url" => "/2253654X/0000004200000004/v2_202311010733/S2253654X2300001X/v2_202311010733/es/main.assets" ] ] "itemSiguiente" => array:19 [ "pii" => "S2253808923000253" "issn" => "22538089" "doi" => "10.1016/j.remnie.2023.02.008" "estado" => "S300" "fechaPublicacion" => "2023-07-01" "aid" => "1429" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2023;42:231-7" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Exploring the opinion of Spanish medical specialists about the usefulness of radiomics in oncology" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "231" "paginaFinal" => "237" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Explorando la opinión de los especialistas Españoles acerca de la utilidad de la radiómica en el área oncológica" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1384 "Ancho" => 3000 "Tamanyo" => 352449 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Analysis of the responses to the open question “<span class="elsevierStyleItalic">What do you think is the main disadvantage of the use of radiomics and other AI technologies in medicine</span>?”. <span class="elsevierStyleBold">A)</span> Count of the 10 most frequent words used. <span class="elsevierStyleBold">B)</span> Word cloud in which the size of each Word is a representation of its frequency within the text compared to the total responses.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "P.M. Cárcamo Ibarra, U.A. López González, A. Esteban Hurtado, N. Orrego Castro, S. Diez Domingo" "autores" => array:5 [ 0 => array:2 [ "nombre" => "P.M." "apellidos" => "Cárcamo Ibarra" ] 1 => array:2 [ "nombre" => "U.A." "apellidos" => "López González" ] 2 => array:2 [ "nombre" => "A." "apellidos" => "Esteban Hurtado" ] 3 => array:2 [ "nombre" => "N." "apellidos" => "Orrego Castro" ] 4 => array:2 [ "nombre" => "S." "apellidos" => "Diez Domingo" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X23000252" "doi" => "10.1016/j.remn.2023.02.001" "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/S2253654X23000252?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808923000253?idApp=UINPBA00004N" "url" => "/22538089/0000004200000004/v1_202307170957/S2253808923000253/v1_202307170957/en/main.assets" ] "itemAnterior" => array:19 [ "pii" => "S2253808923000150" "issn" => "22538089" "doi" => "10.1016/j.remnie.2023.02.002" "estado" => "S300" "fechaPublicacion" => "2023-07-01" "aid" => "1413" "copyright" => "Sociedad Española de Medicina Nuclear e Imagen Molecular" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2023;42:218-22" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Assessment of pulmonary embolism related to active SARS-CoV-2 infection in pregnant women" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "218" "paginaFinal" => "222" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Valoración del tromboembolismo pulmonar relacionado con infección SARS-CoV-2 activa en pacientes embarazadas" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 903 "Ancho" => 2000 "Tamanyo" => 125448 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Pulmonary perfusion SPECT performed in patient 5 showing sub-segmentary perfusion defects in the upper segments of the LLL (A, blue, in coronal and sagittal slices) and LRL (red, in axial and coronal slices), with no correspondence with condensations in the radiological images to justify these findings (B), being compatible with PTE. In addition, a defect of linear horizontal uptake (green arrow) can be seen that continues in the cranial SPECT slices in the posterior direction, radiological corresponding with minimal thickening of the minor fissure (green circle).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "A. Moreno-Ballesteros, Á.C. Rebollo-Aguirre, I. Bolívar-Roldán, T. Busquier, E. Sanchez-de Mora, A. Jimenez-Heffernan" "autores" => array:6 [ 0 => array:2 [ "nombre" => "A." "apellidos" => "Moreno-Ballesteros" ] 1 => array:2 [ "nombre" => "Á.C." "apellidos" => "Rebollo-Aguirre" ] 2 => array:2 [ "nombre" => "I." "apellidos" => "Bolívar-Roldán" ] 3 => array:2 [ "nombre" => "T." "apellidos" => "Busquier" ] 4 => array:2 [ "nombre" => "E." "apellidos" => "Sanchez-de Mora" ] 5 => array:2 [ "nombre" => "A." "apellidos" => "Jimenez-Heffernan" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X22001883" "doi" => "10.1016/j.remn.2022.11.002" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X22001883?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808923000150?idApp=UINPBA00004N" "url" => "/22538089/0000004200000004/v1_202307170957/S2253808923000150/v1_202307170957/en/main.assets" ] "en" => array:21 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Primary tumor heterogeneity on pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "223" "paginaFinal" => "230" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Seda Gülbahar Ateş, Gülay Bilir Dilek, Gülin Uçmak" "autores" => array:3 [ 0 => array:4 [ "nombre" => "Seda" "apellidos" => "Gülbahar Ateş" "email" => array:1 [ 0 => "sdsdglbhr@gmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Gülay" "apellidos" => "Bilir Dilek" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "Gülin" "apellidos" => "Uçmak" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Pathology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author at: University of Health Sciences, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Department of Nuclear Medicine, 06200 Ankara, Turkey." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Heterogeneidad del tumor primario en la <span class="elsevierStyleSup">18</span>F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras terapia neoadyuvante" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1755 "Ancho" => 3341 "Tamanyo" => 543680 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Texture analysis of <span class="elsevierStyleSup">18</span>FDG PET images using LİFEx software<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a>.</p> <p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Patient images were uploaded in DİCOM format. Volume of interests (VOİ) of primary rectal tumors were drawn semi-automatically by using three-dimensional (3D) VOİ tool (purple box on the right panel). Primary tumor lesions were segmented by using 40% of the maximum value in the VOI as a threshold (blue box on the right panel). Texture features were extracted from tumor VOIs using Texture Feature Extraction section (yellow box on the top panel).</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Introduction</span><p id="par0045" class="elsevierStylePara elsevierViewall">Colorectal cancer is the third most common cancer and the second leading cause of cancer-related deaths worldwide<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a>. While the incidence of rectal cancer in especially younger populations has increased in recent years, its mortality rate has decreased due to early diagnosis and advanced treatment protocols<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a>. Locally advanced rectal cancer(LARC) is still an important reason for cancer-related morbidity and mortality. The determination of the disease with a poor prognosis is critical in terms of treatment management at the time of the diagnosis.</p><p id="par0050" class="elsevierStylePara elsevierViewall">The prognostic factors in patients with rectal cancer have been investigated in many studies, and various prognostic clinicopathologic features have been determined. Nevertheless, patient management is still based on the TNM staging system. Treatment responses and prognosis of patients with rectal cancer might be different even in the same TNM stage<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a>. The determination of the patients with poor prognosis or/and poor responses to treatment might change their management. Treatment protocol could be intensified of patients with a higher risk of poor response or unnecessary and ineffective therapy protocols could be avoided in certain patients.</p><p id="par0055" class="elsevierStylePara elsevierViewall">Several studies have existed about the prognostic value of conventional semi-quantitative metabolic values(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis -TLG, etc.) derived from <span class="elsevierStyleSup">18</span>F-FDG PET/CT. Some studies have shown their prognostic importance, but the results are contradictory<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4,5</span></a>. Radiomics is a promising approach for oncology in the era of personalized medicine. Radiomics provides the high-throughput extraction of large amounts of quantitative features of medical images using automated or semi-automated softwares<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a>. Texture analysis which is a prominent technique of radiomics refers to various mathematical methods that may be applied to describe the relationships between the grey level intensity of pixels or voxels and their position within an image<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a>. The texture features might suggest some information such as spatial tumor heterogeneity that is related to tumor characteristics.</p><p id="par0060" class="elsevierStylePara elsevierViewall">Malignant tumors exhibit intra-tumoral biological heterogeneity associated with cellular and molecular characteristics<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a>. It is known that tumor heterogeneity is a significant entity for resistance to treatment and poor prognosis in oncologic diseases<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a>. The identification, characterization and, possibly, treatment of tumor heterogeneity are key challenges in oncology<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9,10</span></a>. The non-invasive assessment of tumoral heterogeneity using textural analysis might play a pivotal role in the management of cancer. The number of the studies have focused on only the patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT) is limited<a class="elsevierStyleCrossRefs" href="#bib0055"><span class="elsevierStyleSup">11–13</span></a>. This study aimed to investigate the value of texture features of primary tumors in staging <span class="elsevierStyleSup">18</span>F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after NAT.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Methods and materials</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Patient population</span><p id="par0065" class="elsevierStylePara elsevierViewall">This retrospective study was approved by the local ethics committee (no: 2022-02/45), and the requirement for informed consent was waived. Forty-four patients with rectal adenocarcinoma who had pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT, and underwent surgery after NAT between 2014 and 2021 were included in this study. The patients who did not receive NAT or/and undergo surgery were excluded from the study. The patients who had metastasis at the time of diagnosis and second primary malignant disease were excluded from the study.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Clinicopathological features</span><p id="par0070" class="elsevierStylePara elsevierViewall">Demographic characteristics and clinical histories of the patients were evaluated from the hospital information system. Tumor histopathologic features were recorded. Pathological tumor regression grades were classified as responders (complete/near complete regression) and non-responders (moderate/minimal/no regression) to NAT. Patients were followed until progression, death, or last medical visit. Follow-up time was calculated from the date of PET/CT to the date of progression, death, lost to follow-up, or last medical visit. Patients’ follow-ups were carried out by radiologic images, <span class="elsevierStyleSup">18</span>F-FDG PET/CT, clinic findings.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">PET/CT acquisition</span><p id="par0075" class="elsevierStylePara elsevierViewall">All patients had fasted for at least 6<span class="elsevierStyleHsp" style=""></span>h before <span class="elsevierStyleSup">18</span>F-FDG PET/CT studies, and the serum glucose levels measured at the time of <span class="elsevierStyleSup">18</span>F-FDG injection were less than 150<span class="elsevierStyleHsp" style=""></span>mg/dL. <span class="elsevierStyleSup">18</span>F-FDG was intravenously administered at a dose of 5.5<span class="elsevierStyleHsp" style=""></span>MBq/kg body weight. PET/CT images were obtained by three-dimensional Siemens Biograph True Point 6 PET/CT device 60<span class="elsevierStyleHsp" style=""></span>min after <span class="elsevierStyleSup">18</span>F-FDG injection. PET scanner and 3<span class="elsevierStyleHsp" style=""></span>mm sliced multidetector CT scanner obtained simultaneous images in the same session. Low dose CT images without intravenous iodinated contrast were used for attenuation correction and anatomical correlation.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Image analysis</span><p id="par0080" class="elsevierStylePara elsevierViewall">All PET/CT images were uploaded to Local Images Features Extraction(LİFEx) software version 7.1.13<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a>. On the PET images, regions of interest (ROIs) were manually drawn to cover the entire volume of primary rectal tumors by two experienced nuclear medicine specialists, and were countered using a threshold of 40% of the SUVmax (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). The conventional PET parameters as SUVmax, SUVmean, MTV, and TLG were measured. LIFEx was run by using the following input parameters: 64 grey levels to resample the ROI content which was performed using absolute resampling in 64 bins and SUV units between a minimum of 0 and a maximum of 40. Spatial resampling was set to 4<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>4<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>4<span class="elsevierStyleHsp" style=""></span>mm. The features extracted from PET images contain first-order parameters(histogram, conventional, discretized) and second-order texture parameters (grey level co-occurrence matrix(GLCM), grey-level run length matrix (GLRLM), neighborhood grey-level difference matrix(NGLDM), grey-level zone length matrix (GLZLM) (summarized in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">The texture parameters obtained from the first-order statistics reflect only the voxel-value frequency distribution. The second approach accounts for the spatial arrangement of the voxel values within the tumor describing this spatial organization. GLCLM texture indices take into account the arrangement of pairs of voxels, while NGLDM texture indices correspond the difference of grey-levels between one voxel and its neighbors. GLRLM indices give the size of homogeneous runs for each grey-level, whereas GLZLM indices provide information on the size of homogeneous zones for each-grey level in 3 or 2 dimensions. The details of different texture parameters were in LİFEx software user guideline<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a>.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Statistical analysis</span><p id="par0090" class="elsevierStylePara elsevierViewall">Statistical analyses were performed using the SPSS software version 21. Optimal cut-off values were determined using the Youden index (sensitivity<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>specifity-1) obtained from ROC curve analyses for each outcome (treatment response to NAT, progression, and mortality). Conventional PET parameters and texture features were divided into two groups based on the optimal cut-off values. Logistic regression analyses were performed to determine predictors of treatment response to NAT in univariate and multivariate analyses, whereas Cox regression analyses were performed to determine predictors of mortality and progression. Highly correlated variables which their correlation coefficient was higher than 0.7 were removed from the analyses to prevent multicollinearity. Univariate regression analyses was performed for each parameter and variable, and those with a p value less than 0.05 or close to 0.05 were included in the multivariate analysis. Survival analysis was performed using Kaplan-Meier method. An overall 5% type-1 error level was used to infer statistical significance.</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Results</span><p id="par0095" class="elsevierStylePara elsevierViewall">Forty-four patients were included in this study. The clinicopathologic characteristics of patients were shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. The mean duration from PET/CT to surgery was 4.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.8 months. The median of follow-up duration was 29.9(interquartile range: 33.6) months. Of the 44 patients, 9(20.5%) showed disease progression (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>4 emergence of metastases; <span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2 local recurrence; <span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>3 metastasis and local recurrence), and 8(18.2%) died of cancer-related reasons during the follow-up period.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Treatment response to neoadjuvant therapy</span><p id="par0100" class="elsevierStylePara elsevierViewall">Parameters found significantly associated with treatment response to NAT in the univariate and multivariate analyses were shown in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>. The univariate results of features not significantly associated with poor response to therapy are given in supplementary <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. SUVmax (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.027), MTV (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.049), and TLG (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.027) values and some texture parameters of primary rectal tumors were found significantly associated with response to NAT in the univariate analyses. The multivariate analysis showed that only difference entropy<span class="elsevierStyleInf">GLCM</span> and correlation<span class="elsevierStyleInf">GLCM</span> parameters were significantly associated with response to NAT.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Progression-free survival</span><p id="par0105" class="elsevierStylePara elsevierViewall">Median progression-free survival(PFS) was not reached at the end of the study. PFS was 94.8% at 1<span class="elsevierStyleHsp" style=""></span>year, 85.4% at 2 years, 71% at 3 years. pN2 stage (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.017), positive surgical margin (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.003), higher TLG (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.044) and some texture parameters were associated with progression in patients with rectal cancer in the univariate analysis (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>). The univariate results of features not significantly associated with PFS are given in supplementary <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. In the multivariate analysis, the positivity of surgical margin, intensity interquartile range<span class="elsevierStyleInf">CONV</span> and AUC-CSH<span class="elsevierStyleInf">DISC</span> (the area under a cumulative SUV-volume histograms) texture parameters were independent predictors of progression in the patients with rectal cancer.</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Overall survival</span><p id="par0110" class="elsevierStylePara elsevierViewall">Median overall survival(OS) was not reached at the end of the study. It was found that OS was 97.7% at 1<span class="elsevierStyleHsp" style=""></span>year, 94.9% at 2 years, 88.2% at 3 years, and 72.2% at 5 years. Parameters significantly associated with mortality in the univariate and multivariate analyses were shown in <a class="elsevierStyleCrossRef" href="#tbl0025">Table 5</a>. In the univariate analyses, pN1 stage, MTV less than 20.5, and some texture parameters were significantly associated with mortality. Normalized inverse difference<span class="elsevierStyleInf">GLCM</span> and LZLGE<span class="elsevierStyleInf">GLZLM</span> parameters were found as independent predictors of mortality in patients with rectal cancer. The univariate results of features not significantly associated with OS are given in supplementary <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>.</p><elsevierMultimedia ident="tbl0025"></elsevierMultimedia></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Discussion</span><p id="par0115" class="elsevierStylePara elsevierViewall">In the present study, clinicopathologic features, conventional and textural PET parameters were evaluated together to predict poor response to therapy, PFS, and OS in rectal adenocarcinoma patients who underwent surgery after NAT. It was found that especially only some texture parameters were independent predictors of these outcomes. The selected texture parameters obtained from PET images had a robust prognostic value for patients with rectal cancer who underwent surgery after NAT.</p><p id="par0120" class="elsevierStylePara elsevierViewall">Many studies have attempted to demonstrate the potential prognostic value of conventional PET parameters of primary rectal tumors with conflicting results<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">16–19</span></a>. In this study, TLG and MTV were associated with PFS and OS in univariate analyses, respectively. However, neither of these were revealed as independent prognostic factors in multivariate analyses. Likewise, the study by Hotta et al. demonstrated that MTV and TLG were associated with both OS and PFS, and also SUVmax was related to PFS only in univariate analyses in patients with rectal cancer who underwent surgery<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a>. However, the study about PET volumetric parameters by Choi et al. showed that MTV and TLG were independent predictors of both PFS and OS in patients with rectal cancer<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a>. On the contrary, Lovinfosse et al. found that none of the conventional PET features were significantly associated with PFS, and only SUVmean was an independent predictor of OS among conventional PET parameters in patients with LARC<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a>. Similar to the literature<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12,20,21</span></a>, the higher values of MTV and TLG were associated with a poor response to therapy in this study. In addition to these studies, we found that higher SUVmax was related to a poor response to therapy. MTV and TLG are metabolic volumetric parameters reflecting metabolic tumor burden whereas SUVmax represents FDG uptake and indirectly tumor aggressiveness. These semi-quantitative parameters may be related to treatment response, but none of those was an independent predictor for response to NAT. These inconsistent results about the conventional PET parameters may be due to differences among the patient populations and study designs. Given the diverse findings, despite the prognostic importance of conventional PET parameters, their definite prognostic values are contradictory.</p><p id="par0125" class="elsevierStylePara elsevierViewall">Texture analysis is a variety of mathematical methods that may be applied to describe relationships between the grey level intensity of pixels or voxels and their position within an image. Textural parameters are mostly obtained by statistics-based techniques, and statistical methods are categorized into first-order(one pixel), second and higher-order(two and more pixels) statistics<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a>. While the texture parameters obtained from the first-order statistics reflect only the voxel-value frequency distribution, the second approach accounts for the spatial arrangement of the voxel values within the tumor describing this spatial organization<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a>. It was hypothesized that the uptake distribution within the tumor could bring more knowledge about the tumor than a single SUV or tumor volume<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a>. A number of textural parameters can provide a measure of intralesional heterogeneity<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7,23</span></a>. Although the meanings of texture parameters in terms of biological features have not been known totally yet, they have been investigated in different malignancies including rectal cancer to determine cancer diagnosis, prognosis, and evaluation of treatment response. A limited number of studies with rectal cancer patients are available so far to predict prognosis<a class="elsevierStyleCrossRefs" href="#bib0055"><span class="elsevierStyleSup">11,13,19</span></a>.</p><p id="par0130" class="elsevierStylePara elsevierViewall">In the present study, AUC-CSH was the most robust parameter among the first-order features. AUC-CSH was associated with response to NAT, PFS, and OS in univariate analyses; however, it was an independent predictor for only PFS in patients with rectal cancer in multivariate analysis. It was known that AUC-CSH is a quantitative index of tracer uptake heterogeneity and/or heterogeneous response<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a>. The study on repeatability analysis of features in patients with non-small cell lung cancer showed that AUC-CSH was the most repeatable parameter among the first-order features<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a>. Moreover, in the present study, the first-order parameters of intensity interquartile range<span class="elsevierStyleInf">CONV</span>, intensity-based energy<span class="elsevierStyleInf">CONV</span>, and histogram entropy log10<span class="elsevierStyleInf">DISC</span> were associated with poor response to therapy and PFS. Intensity-based energy<span class="elsevierStyleInf">CONV</span> was an independent predictor for PFS in patients with rectal cancer. Energy parameters reflect the uniformity of the distribution<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a>. Higher intensity-based energy<span class="elsevierStyleInf">CONV</span> was associated with longer PFS in our study, which might support the association between tumor heterogeneity and poor outcome. Buvat et al. suggested that the first-order statistics-entropy(histogram-derived) is one of the heterogeneity descriptors which should be focused on in studies<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a>, and Desseroit et al. found that it was one of the most reliable parameters among the first-order features<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a>. However, histogram entropy log10<span class="elsevierStyleInf">DISC</span> was statistically significant only in univariate analyses in our study.</p><p id="par0135" class="elsevierStylePara elsevierViewall">Bang et al. found that the kurtosis of the absolute gradient(GrKurtosis) which is a first-order histogram intensity feature was an independent predictor of 3-year disease-free survival in patients with rectal cancer<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a>. It reflects the peakedness of the gradient in the images<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a>. The study by Lovinfosse et al. showed that excess kurtosis was associated with disease-free survival in univariate analysis<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a>. Our study has shown no association between excess intensity kurtosis, intensity kurtosis, and its outcomes. The discrepancies among studies might be due to the fact that some first-order texture parameters lead to poor repeatability and reproducibility<a class="elsevierStyleCrossRefs" href="#bib0125"><span class="elsevierStyleSup">25–27</span></a>. The first-order parameters do not reflect the spatial arrangement of voxels. Therefore, it should be considered while the first-order parameters were included in future studies about texture analysis.</p><p id="par0140" class="elsevierStylePara elsevierViewall">Entropy<span class="elsevierStyleInf">GLCM</span> which is one of the important second-order texture parameters has been examined in many studies. In the present study, entropy<span class="elsevierStyleInf">GLCM</span> was significantly associated with response to NAT and PFS only in univariate analyses. Nevertheless, difference entropy<span class="elsevierStyleInf">GLCM</span> was found as an independent predictor for response to NAT in our study. In parallel to our results, Buvat et al. suggested that difference entropy<span class="elsevierStyleInf">GLCM</span> was one of the most robust heterogeneity descriptors<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a>. GLCM reflects the probability of observing a pair of values in voxels at a given distance in a given direction, and entropy is the randomness of grey-level voxel pairs<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">15,28</span></a>. The studies about the robustness and reproducibility of the texture parameters confirmed that GLCM entropy was one of the most robust and reproducible parameters<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">29–31</span></a>. It is known that heterogeneous lesions have a higher value of entropy than visually homogeneous lesions<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a>. It was demonstrated that the texture indices including entropy measured from autoradiography images were sensitive to the cell pattern seen on a histologic slice<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">32,33</span></a>. Even though its biological meaning has not been understood exactly yet, there are studies showing the prognostic value of entropy<span class="elsevierStyleInf">GLCM</span> in rectal cancer. The study by Hotta et al. found that the parameter of entropy<span class="elsevierStyleInf">GLCM</span> was an independent predictor factor for both OS and PFS in patients with rectal cancer treated by surgery<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a>. On the other hand, the study by Lovinfosse demonstrated the significant association between entropy<span class="elsevierStyleInf">GLCM</span> and disease-specific survival only in the univariate analysis, but there was no significant association between entropy<span class="elsevierStyleInf">GLCM</span> and disease-free survival(DFS), OS, response to NAT in patients with LARC<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a>. Bang et al. found that while sum entropy<span class="elsevierStyleInf">GLCM</span> and entropy<span class="elsevierStyleInf">GLCM</span> were associated with response to NAT in patients with LARC in univariate analyses, there was no association between 3-year DFS and entropy<span class="elsevierStyleInf">GLCM</span><a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a>. The study by Martin-Gonzalez indicated that there was no significant association between entropy<span class="elsevierStyleInf">GLCM</span> and response to NAT in patients with LARC<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a>. These discrepancies may be due to the differences in populations and outcomes among the studies. Despite the different results, it seems that entropy<span class="elsevierStyleInf">GLCM</span> was one of the important prognostic heterogeneity descriptors among other parameters for patients with rectal cancer. Especially difference entropy<span class="elsevierStyleInf">GLCM</span> is an independent prognostic factor for response to NAT in rectal cancer patients according to our results.</p><p id="par0145" class="elsevierStylePara elsevierViewall">We found that there was a significant association between coarseness<span class="elsevierStyleInf">NGLDM,</span> GLNU<span class="elsevierStyleInf">GLZM</span> and response to NAT in parallel to the other studies<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">20,21</span></a>. LRHGE<span class="elsevierStyleInf">GLRM</span> and correlation<span class="elsevierStyleInf">GLCM</span> were significantly associated with response to NAT, but only correlation<span class="elsevierStyleInf">GLCM</span> was found as an independent predictor of response to NAT among these second-order parameters. It seems that correlation<span class="elsevierStyleInf">GLCM</span> and difference entropy<span class="elsevierStyleInf">GLCM</span> were the most robust second-order texture parameters for the prediction of response to NAT in patients with rectal cancer who underwent surgery after NAT.</p><p id="par0150" class="elsevierStylePara elsevierViewall">Normalized inverse difference<span class="elsevierStyleInf">GLCM</span> and LZLGE<span class="elsevierStyleInf">GLZM</span> were found as independent predictors for OS in patients with rectal cancer who underwent surgery after NAT. Normalized inverse difference<span class="elsevierStyleInf">GLCM</span> is known as one of the most robust heterogeneity descriptors<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a>. In the study by Lovinfosse et al., the parameters of contrast<span class="elsevierStyleInf">NGLDM</span> and dissimilarity<span class="elsevierStyleInf">GLCM</span> were found as independent predictors of OS<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a>. In this study, these were associated with OS in the univariate analysis in parallel to their study. While contrast<span class="elsevierStyleInf">NGLDM</span> is the intensity difference between neighboring regions, dissimilarity<span class="elsevierStyleInf">GLCM</span> is the variation of grey-level voxel pairs<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a>. Orlhac et al. showed that these were indices presenting a high correlation, and these indices were grouped in the same group<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a>. The correlation between contrast<span class="elsevierStyleInf">NGLDM</span> and dissimilarity<span class="elsevierStyleInf">GLCM</span> might explain the similar performance in the prediction of OS in this study. The study by Orlhac et al. suggested that the same group indices which describe highly correlated information might not be calculated in studies<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a>, which is consistent with our study results. Such limitations about feature selection could be overcome by using advanced feature selection methods in future studies.</p><p id="par0155" class="elsevierStylePara elsevierViewall">This study has three main limitations. Firstly, this study is a retrospective study with a limited number of patients. Only a certain group of rectal cancer patients who underwent surgery after NAT were included in this study because surgery is a main prognostic factor for patients with rectal cancer. Secondly, the large number of features requires effective methods for their selection to avoid misinterpretation problems. Besides, model training and validation might be performed in data sets. We could not carry out these advanced methods due to the limited number of patients. Finally, it is unclear how texture parameters are actually related to the biological tumor features. We could not examine the histopathologic-genetic tumor characteristics because of the nature of the study. There is a need for more studies comparing histopathologic findings and texture features. Nevertheless, this study has provided promising results about the predictive value of texture analysis obtained from pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT in patients with rectal cancer.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conclusion</span><p id="par0160" class="elsevierStylePara elsevierViewall">The certain texture parameters obtained from pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer. In the future, the integration of texture analysis into patient management might be provided and opened up new possibilities for personalized medicine.</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Conflict of interest</span><p id="par0165" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:13 [ 0 => array:3 [ "identificador" => "xres1934155" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Purpose" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1666969" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec1666968" "titulo" => "Abbreviations" ] 3 => array:3 [ "identificador" => "xres1934154" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusión" ] ] ] 4 => array:2 [ "identificador" => "xpalclavsec1666967" "titulo" => "Palabras clave" ] 5 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 6 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods and materials" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Patient population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Clinicopathological features" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "PET/CT acquisition" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Image analysis" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 7 => array:3 [ "identificador" => "sec0040" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Treatment response to neoadjuvant therapy" ] 1 => array:2 [ "identificador" => "sec0050" "titulo" => "Progression-free survival" ] 2 => array:2 [ "identificador" => "sec0055" "titulo" => "Overall survival" ] ] ] 8 => array:2 [ "identificador" => "sec0060" "titulo" => "Discussion" ] 9 => array:2 [ "identificador" => "sec0065" "titulo" => "Conclusion" ] 10 => array:2 [ "identificador" => "sec0070" "titulo" => "Conflict of interest" ] 11 => array:2 [ "identificador" => "xack677804" "titulo" => "Acknowledgements" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2022-10-17" "fechaAceptado" => "2023-01-03" "PalabrasClave" => array:2 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1666969" "palabras" => array:5 [ 0 => "Rectal cancer" 1 => "<span class="elsevierStyleSup">18</span>F-FDG PET/CT" 2 => "Texture analysis" 3 => "Radiomics" 4 => "Prognosis" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec1666968" "palabras" => array:15 [ 0 => "LARC" 1 => "<span class="elsevierStyleSup">18</span>F-FDG" 2 => "PET" 3 => "CT" 4 => "SUVmax" 5 => "MTV" 6 => "TLG" 7 => "NAT" 8 => "ROİ" 9 => "GLCM" 10 => "GLRLM" 11 => "NGLDM" 12 => "GLZLM" 13 => "PFS" 14 => "OS" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1666967" "palabras" => array:5 [ 0 => "Cáncer de recto" 1 => "<span class="elsevierStyleSup">18</span>F-FDG PET/TC" 2 => "Análisis de textura" 3 => "Radiómica" 4 => "Pronóstico" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Purpose</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT).</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Patients with rectal cancer who had pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Forty-four patients (26(59%) male, 18(41%) female; 60.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were 15(34.9%) and 28(65.1%), respectively. One patient’ pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropy<span class="elsevierStyleInf">GLCM</span> and correlation<span class="elsevierStyleInf">GLCM</span> parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile range<span class="elsevierStyleInf">CONV</span> and AUC-CSH<span class="elsevierStyleInf">DISC</span> texture parameters were independent predictors of progression, while normalized inverse difference<span class="elsevierStyleInf">GLCM</span> and LZLGE<span class="elsevierStyleInf">GLZLM</span> parameters were independent predictors of mortality.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">The texture parameters obtained from pretreatment <span class="elsevierStyleSup">18</span>F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Purpose" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Estudio retrospectivo cuyo objetivo fue investigar el valor de las características de textura de los tumores primarios en la PET/TC con <span class="elsevierStyleSup">18</span>F-FDG pretratamiento para la predicción de la respuesta al tratamiento, la progresión y la supervivencia gobal en pacientes con cáncer de recto que se sometieron a cirugía después de la terapia neoadyuvante (TNA).</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Métodos</span><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Se incluyeron en este estudio pacientes con cáncer de recto que se sometieron a estudio PET/TC con <span class="elsevierStyleSup">18</span>F-FDG antes del tratamiento y se sometieron a cirugía después de TNA. Se registraron las características clínico-patológicas, la fecha del último seguimiento, la evolución y fallecimiento Los parámetros de las texturas y los convencionales de PET (Standard Uptake Value-SUVmax, volumen tumoral metabólico-MTV, glucólisis total de la lesión-TLG) se obtuvieron a partir de imágenes PET/TC utilizando el programa LifeX. Los parámetros se agruparon utilizando el índice de Youden en el análisis ROC. Los factores que predicen la respuesta patológica al tratamiento, la progresión y la supervivencia global se determinaron mediante regresión logística y análisis de regresión de Cox.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Cuarenta y cuatro pacientes (26-59% hombres, 18-41% mujeres; 60,1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11,4 años) con cáncer de recto fueron incluidos en este estudio. El número de pacientes respondedores y no respondedores a TNA fueron de 15 (34,9%) y 28 (65,1%), respectivamente. La mediana de la duración del seguimiento fue de 29,9 meses. 9 (20,5%) mostraron progresión de la enfermedad y 8 (18,2%) fallecieron durante el período de seguimiento. Los parámetros de entropía GLCM de diferencia y correlación GLCM se encontraron como predictores independientes para la respuesta a TNA Los parámetros de positividad del margen quirúrgico, rango intercuartílico de intensidad CONV y textura AUC-CSH<span class="elsevierStyleInf">DISC</span> fueron predictores independientes de progresión, mientras que los parámetros de diferencia inversa normalizada GLCM y LZLGE<span class="elsevierStyleInf">GLZLM</span> fueron predictores independientes de mortalidad.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusión</span><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Los parámetros de textura obtenidos de la PET/TC con <span class="elsevierStyleSup">18</span>F-FDG pretratamiento han presentado un valor predictivo más robusto que los parámetros convencionales de la PET en pacientes con cáncer de recto que se sometieron a cirugía después de TNA.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusión" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "<p id="par0180" class="elsevierStylePara elsevierViewall">The following is Supplementary data to this article:<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>" "etiqueta" => "Appendix A" "titulo" => "Supplementary data" "identificador" => "sec0080" ] ] ] ] "multimedia" => array:7 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1755 "Ancho" => 3341 "Tamanyo" => 543680 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Texture analysis of <span class="elsevierStyleSup">18</span>FDG PET images using LİFEx software<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a>.</p> <p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Patient images were uploaded in DİCOM format. Volume of interests (VOİ) of primary rectal tumors were drawn semi-automatically by using three-dimensional (3D) VOİ tool (purple box on the right panel). Primary tumor lesions were segmented by using 40% of the maximum value in the VOI as a threshold (blue box on the right panel). Texture features were extracted from tumor VOIs using Texture Feature Extraction section (yellow box on the top panel).</p>" ] ] 1 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0010" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Abbreviations:</span> AUC-CSH: area under curve-cumulative SUV-volume histograms, Q1: the first quartile, Q2: the second quartile, Q3: the third quartile, GLCM: grey level co-occurrence matrix, GLRLM: grey-level run length matrix, SRE: short-run emphasis, LRE: long-run emphasis, LGRE: low grey-level run emphasis, HGRE: high grey-level run emphasis, SRLGE: short-run low grey-level emphasis, SRHGE: short-run high grey-level emphasis, LRLGE: long-run low grey-level emphasis, LRHGE: long-run high grey-level emphasis, GLNU: grey-level non-uniformity for run, RLNU: run length non-uniformity, RP: run percentage, NGLDM: neighborhood grey-level difference matrix, GLZLM: grey-level zone length matrix, SZE: short-zone emphasis, LZE: long-zone emphasis, LGZE: low grey-level zone emphasis, HGZE: high grey-zone emphasis, SZLGE: short-zone low grey-level emphasis, SZHGE: short-zone high grey-level emphasis, LZLGE: long-zone low grey-level emphasis, LZHGE: long-zone high grey-level emphasis, GLNU: grey-level non-uniformity for zone, ZLNU: zone length non-uniformity, ZP: zone percentage.</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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="2" align="left" valign="middle">First order</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Histogram \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Skewness, Kurtosis, Entropy log10, Entropy log2, Energy, AUC-CSH \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Conventional/Discretized \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">minimum, mean, SD, maximum, Q1, Q2, Q3, Peak 0.5<span class="elsevierStyleHsp" style=""></span>ml, Peak 1<span class="elsevierStyleHsp" style=""></span>ml \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="4" align="left" valign="middle">Second order</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GLCM \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Homogeneity, Energy, Contrast, Correlation, Entropy, Dissimilarity \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GLRLM \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SRE, LRE, LGRE, HGRE, SRLGE, SRHGE, LRLGE, LRHGE, GLNU, RLNU, RP \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NGLDM \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Coarseness, Contrast, Busyness \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GLZLM \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SZE, LZE, LGZE, HGZE, SZLGE, SZHGE, LZLGE, LZHGE, GLNUz, ZLNU, ZP \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3222222.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The texture parameters extracted from PET images.</p>" ] ] 2 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0015" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Abbreviations:</span> SD: standard deviation, NAT: neoadjuvant therapy.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Features</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (mean<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD)</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">60.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="2" align="left" valign="middle">Gender</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Male \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 (59%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Female \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (41%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="6" align="left" valign="middle">Neoadjuvant therapy</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CRT (chemo-radiotherapy) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">41 (93.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">•</span><p id="par0005" class="elsevierStylePara elsevierViewall">Capecitabine</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (9.1%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">•</span><p id="par0010" class="elsevierStylePara elsevierViewall">Capecitabine<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>oxaliplatin</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31 (70.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">•</span><p id="par0015" class="elsevierStylePara elsevierViewall">Fluorouracil<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>oxaliplatin</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (11.4%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">•</span><p id="par0020" class="elsevierStylePara elsevierViewall">Fluorouracil</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (2.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">RT (radiotherapy) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (6.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="4" align="left" valign="middle">Tumor grade</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Well-differentiated \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (25%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderately-differentiated \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17 (38.6%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Poorly-differentiated \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (6.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Non-defined \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (29.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mucinous adenocarcinoma/mucinous component</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (13.6%)-2 (45.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="2" align="left" valign="middle">Pathological T stage (pT)</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pT0-2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (52.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pT3-4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 (47.7%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="3" align="left" valign="middle">Pathological N stage (pN)</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pN0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35 (79.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pN1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (18.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pN2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (2.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">The positivity of surgical margin</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (6.8%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Lymphovascular invasion (LVI)</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (2.3%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Perineural invasion (PNI)</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (4.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " rowspan="2" align="left" valign="middle">Pathological response grade</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NAT responders \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (34.9%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NAT non-responders \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28 (65.1%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Progression</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (20.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Death</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (18.2%) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3222220.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">The Clinicopathologic Characteristics.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Abbreviations:</span> SUVmax: maximum standardized uptake value, MTV: metabolic tumor volume, TLG: total lesion glycolysis, CONV: conventional, DISC: discretized, AUC-CSH: area under curve-cumulative SUV-volume histograms, GLCM: grey level co-occurrence matrix, LRHGE: long-run high grey-level emphasis, GLRLM: grey-level run length matrix, NGLDM: neighborhood grey-level difference matrix, GLNU: grey-level non-uniformity, GLZLM: grey-level zone length matrix.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " rowspan="2" align="left" valign="middle" scope="col" style="border-bottom: 2px solid black">Parameters</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Univariate</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Multivariate</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% C.I. \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% C.I. \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SUVmax (>20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.23−34.38) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.027 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MTV (>16.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.75 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.003−14.02) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.049 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TLG (>233) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.23−30.28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.027 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Intensity interquartile range<span class="elsevierStyleInf">CONV</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.17−17.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.028 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Intensity based energy<span class="elsevierStyleInf">CONV</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.81 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.44−23.36) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.013 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Histogram entropy log10<span class="elsevierStyleInf">DISC</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.04−79.01) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.046 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AUC-CSH<span class="elsevierStyleInf">DISC</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.23−23.19) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.026 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Entropy log2<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.23−34.28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.027 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Difference entropy<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.87−220.94) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.004 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.94 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.75−244.81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.004 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Correlation<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.23−34.28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.027 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.04−44.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.045 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LRHGE<span class="elsevierStyleInf">GLRLM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.52−23.67) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.011 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Coarseness<span class="elsevierStyleInf">NGLDM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.18−17.21) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.028 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GLNU<span class="elsevierStyleInf">GLZLM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.06−20.01) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.041 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3222219.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Parameters significantly associated with poor response to neoadjuvant therapy in the univariate and multivariate logistic regression analyses.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Abbreviations:</span> pN: pathologic N stage, TLG: total lesion glycolysis, CONV: conventional, DISC: discretized, AUC-CSH: area under curve-cumulative SUV-volume histograms, GLCM: grey-level co-occurrence matrix, RLNU: run length non-uniformity, GLRLM: grey-level run length matrix.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " rowspan="2" align="left" valign="middle" scope="col" style="border-bottom: 2px solid black">Parameters</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Univariate</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Multivariate</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% C.I. \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% C.I. \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pN (pN0: reference) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.017 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0025"><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">•</span><p id="par0025" class="elsevierStylePara elsevierViewall">pN1</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.95 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.68−12.68) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.147<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0030"><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">•</span><p id="par0030" class="elsevierStylePara elsevierViewall">pN2</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.99−883.57) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.007 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">The positivity of surgical margin \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.81−151.66) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.003 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.30−191.98) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TLG (>286.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.04−14.46) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.044 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Intensity interquartile range<span class="elsevierStyleInf">CONV</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.68 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.93−70.64) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.007 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Intensity based energy<span class="elsevierStyleInf">CONV</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.72−57.57) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.83−98.71) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.002 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Histogram entropy log10<span class="elsevierStyleInf">DISC</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.02−18.82) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.047 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AUC-CSH<span class="elsevierStyleInf">DISC</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.35−18.77) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.016 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.18−22.23) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.029 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Variance<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.75 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.17−19.25) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.029 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Entropy log10<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.12−18.24) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.034 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">RLNU<span class="elsevierStyleInf">GLRLM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.42−91.61) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.022 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3222221.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">It is not statistically significant.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Parameters significantly associated with progression in the univariate and multivariate Cox regression analysis.</p>" ] ] 5 => array:8 [ "identificador" => "tbl0025" "etiqueta" => "Table 5" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleBold">Abbreviations:</span> pN: pathologic N stage, MTV: metabolic tumor volume, AUC-CSH: area under curve-cumulative SUV-volume histograms, DISC: discretized, GLCM: grey level co-occurrence matrix, NGLDM: neighborhood grey-level difference matrix, LZLGE: long-zone low grey-level emphasis, GLZLM: grey-level zone length matrix.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " rowspan="2" align="left" valign="middle" scope="col" style="border-bottom: 2px solid black">Parameters</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Univariate</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Multivariate</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% C.I. \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% C.I. \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">pN (pN0: reference) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.032 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0035"><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">•</span><p id="par0035" class="elsevierStylePara elsevierViewall">pN1</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.63−29.44) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.009 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><ul class="elsevierStyleList" id="lis0040"><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">•</span><p id="par0040" class="elsevierStylePara elsevierViewall">pN2</p></li></ul> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.992<a class="elsevierStyleCrossRef" href="#tblfn0010">*</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MTV (>20.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.3−0.93) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.041 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AUC-CSH<span class="elsevierStyleInf">DISC</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.97 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.98−16.03) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.053 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Dissimilarity<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.10−83.58) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.041 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Normalized inverse difference<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.53−31.25) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.012 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.71−256.95) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Correlation<span class="elsevierStyleInf">GLCM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.34−21.59) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.017 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Contrast<span class="elsevierStyleInf">NGLDM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.16−31.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.033 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LZLGE<span class="elsevierStyleInf">GLZLM</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1.60−40.28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.011 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31.45 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.91−340.08) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3222218.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">It is not statistically significant.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Parameters significantly associated with mortality in the univariate and multivariate analyses.</p>" ] ] 6 => array:5 [ "identificador" => "upi0005" "tipo" => "MULTIMEDIAECOMPONENTE" "mostrarFloat" => false "mostrarDisplay" => true "Ecomponente" => array:2 [ "fichero" => "mmc1.docx" "ficheroTamanyo" => 23724 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:33 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "H. 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