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Patient # 18: 64 years, female, breast cancer, focal uptake in parietal thickening of ascending cecum, SUVm of 8.2. FCC: mucous ulcerations of the ascending cecum. Biopsy: chronic inflammatory disease compatible with Crohn's disease, tuberculosis is ruled out.</p> <p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">(a–c) Fusion sections: axial, coronal and sagittal. (d) MIP image.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "L. Servente, V. Gigirey, M. García Fontes, O. Alonso" "autores" => array:4 [ 0 => array:2 [ "nombre" => "L." "apellidos" => "Servente" ] 1 => array:2 [ "nombre" => "V." "apellidos" => "Gigirey" ] 2 => array:2 [ "nombre" => "M." "apellidos" => "García Fontes" ] 3 => array:2 [ "nombre" => "O." "apellidos" => "Alonso" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X16302281" "doi" => "10.1016/j.remn.2017.03.012" "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/S2253654X16302281?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808917301283?idApp=UINPBA00004N" "url" => "/22538089/0000003700000001/v1_201801100554/S2253808917301283/v1_201801100554/en/main.assets" ] "itemAnterior" => array:19 [ "pii" => "S225380891730143X" "issn" => "22538089" "doi" => "10.1016/j.remnie.2017.10.017" "estado" => "S300" "fechaPublicacion" => "2018-01-01" "aid" => "916" "copyright" => "Elsevier España, S.L.U. and SEMNIM" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Esp Med Nucl Imagen Mol. 2018;37:3-8" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 1 "HTML" => 1 ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Concordance between brain <span class="elsevierStyleSup">18</span>F-FDG PET and cerebrospinal fluid biomarkers in diagnosing Alzheimer's disease" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "3" "paginaFinal" => "8" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Concordancia entre la PET cerebral con <span class="elsevierStyleSup">18</span>F-FDG y los biomarcadores en líquido cefalorraquídeo en el diagnóstico de enfermedad de Alzheimer" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3693 "Ancho" => 1611 "Tamanyo" => 210702 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Box plots of the values of each CSF biomarker according to the hypometabolic pattern found by FDG-PET. Indet.: undetermined; p[ED]: pattern of Alzheimer's disease; p[no-EA]: non Alzheimer's disease pattern.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "S. Rubí, A. Noguera, S. Tarongí, M. Oporto, A. García, H. Vico, A. Espino, M.J. Picado, A. Mas, C. Peña, G. Amer" "autores" => array:11 [ 0 => array:2 [ "nombre" => "S." "apellidos" => "Rubí" ] 1 => array:2 [ "nombre" => "A." "apellidos" => "Noguera" ] 2 => array:2 [ "nombre" => "S." "apellidos" => "Tarongí" ] 3 => array:2 [ "nombre" => "M." "apellidos" => "Oporto" ] 4 => array:2 [ "nombre" => "A." "apellidos" => "García" ] 5 => array:2 [ "nombre" => "H." "apellidos" => "Vico" ] 6 => array:2 [ "nombre" => "A." "apellidos" => "Espino" ] 7 => array:2 [ "nombre" => "M.J." "apellidos" => "Picado" ] 8 => array:2 [ "nombre" => "A." "apellidos" => "Mas" ] 9 => array:2 [ "nombre" => "C." "apellidos" => "Peña" ] 10 => array:2 [ "nombre" => "G." "apellidos" => "Amer" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X16302372" "doi" => "10.1016/j.remn.2017.05.003" "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/S2253654X16302372?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S225380891730143X?idApp=UINPBA00004N" "url" => "/22538089/0000003700000001/v1_201801100554/S225380891730143X/v1_201801100554/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Can metabolic tumour parameters on primary staging <span class="elsevierStyleSup">18</span>F-FDG-PET/CT contribute to risk stratification of primary central nervous system lymphomas for patient management as a prognostic model?" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "9" "paginaFinal" => "14" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "K. Okuyucu, E. Alagoz, S. Ince, S. Ozaydin, N. Arslan" "autores" => array:5 [ 0 => array:3 [ "nombre" => "K." "apellidos" => "Okuyucu" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 1 => array:3 [ "nombre" => "E." "apellidos" => "Alagoz" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 2 => array:4 [ "nombre" => "S." "apellidos" => "Ince" "email" => array:1 [ 0 => "drsemra@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" ] ] ] 3 => array:3 [ "nombre" => "S." "apellidos" => "Ozaydin" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 4 => array:3 [ "nombre" => "N." "apellidos" => "Arslan" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Nuclear Medicine Department, Gulhane Training and Research Hospital, Ankara, Turkey" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Medical Oncology Department, Gulhane Training and Research Hospital, Ankara, Turkey" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "¿Pueden contribuir los parámetros tumorales metabólicos, establecidos mediante <span class="elsevierStyleSup">18</span>F-FDG-PET/TC para estadificación primaria, a la estratificación del riesgo de los linfomas del sistema nervioso central, para el tratamiento del paciente como modelo pronóstico?" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1841 "Ancho" => 1583 "Tamanyo" => 119040 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier curve for TLG associated with overall survival.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Primary central nervous system (CNS) lymphoma is an aggressive and fatal extranodal non-Hodgkin lymphoma (NHL) jailed in CNS at initial diagnosis. Primary CNS lymphoma (PCNSL) is a rare form of the disease accounting for 3–4% of all primary brain tumours and 4–6% of extranodal lymphomas.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">1</span></a> Its prognosis is poor and the disease has a fatal outcome when compared with systemic NHL.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">2</span></a> A few baseline risk stratification scoring systems have been suggested to estimate the prognosis mainly based on serum lactate dehydrogenase (LDH) level, age, Karnofsky performance score (KPS), involvement of deep brain structures and cerebrospinal fluid (CSF) protein concentration.<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">3,4</span></a> Recently several other prognostic factors such as serologic markers, tumour characteristics and pharmacokinetic parameters of methotrexate have been reported to stratify risk groups.<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">5,6</span></a> Nevertheless these preliminary studies need external validation with larger cohort studies.</p><p id="par0010" class="elsevierStylePara elsevierViewall">FDG-PET/CT has been used extensively for baseline staging, restaging, evaluation of interim and post-therapy response in many malignancies the same as in primary CNS NHL.<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">7</span></a> It also has a high prognostic value regarding overall survival (OS) and disease-free survival (DFS) in many cancers and lymphomas. In this study, we aimed to investigate the prognostic value of metabolic tumour indexes on the primary staging FDG-PET/CT of patients with PCNSL comparing them with recently validated prognosticators.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><p id="par0015" class="elsevierStylePara elsevierViewall">Disseminated nodal disease secondarily involving CNS or primary CNS lymphoma with extracranial involvement were not included in the study. From 2006 to 2015, the clinical history of 24 patients having PCNSL were examined retrospectively. Three patients with primary ocular origin with Mantle cell or MALT type and 7 patients who did not have a baseline FDG-PET were excluded from the study. Finally, 14 patients with primary CNS diffuse large B-cell lymphoma (DLBCL) (stage I) were enrolled in this retrospective cohort study. The cases were histopathologically proven by excisional biopsy. The study was conducted at the Nuclear Medicine Department of Gülhane Training and Research Hospital, Ankara. Primary staging FDG-PET (in two cases) or FDG-PET/CT (in 12 cases) were performed for all patients before the treatment. The patients were treated with high dose methotrexate and followed up by Medical Oncology Department of our hospital.</p><p id="par0020" class="elsevierStylePara elsevierViewall">Quantitative parameters like maximum standardized uptake value (SUVmax), average standardized uptake value (SUVmean), metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were calculated for all patients before the treatment on initial staging FDG-PET/CT. We retrospectively examined the response to treatment and the outcome of the patients. OS was defined as the time from diagnosis to death by any cause, including those not related to the disease or last follow-up. DFS was defined as the time from diagnosis to the detection of relapse or last follow-up. Informed consent was considered unnecessary as it was a retrospective study using records, documents and data of patients referred to our clinic for the test. Cox regression models were performed to determine the relation of metabolic parameters with DFS and OS time.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075"><span class="elsevierStyleSup">18</span>F-FDG-PET/CT imaging protocol</span><p id="par0025" class="elsevierStylePara elsevierViewall">Patients fasted for 6<span class="elsevierStyleHsp" style=""></span>h and their blood glucose level had to be under 150<span class="elsevierStyleHsp" style=""></span>mg/dl before the injection of an activity of 370–555<span class="elsevierStyleHsp" style=""></span>MBq of <span class="elsevierStyleSup">18</span>F-FDG according to patient's weight. Image acquisitions were performed 1<span class="elsevierStyleHsp" style=""></span>h later with an integrated PET/CT scanner (Discovery 690-GE Healthcare). Unenhanced low dose CT and PET emission data were acquired from mid-thigh to the vertex of the skull in supine position with the arms raised over head. CT data was obtained by automated dose modulation of 120<span class="elsevierStyleHsp" style=""></span>kVp (maximal 100<span class="elsevierStyleHsp" style=""></span>mA), collimation of 64<span class="elsevierStyleHsp" style=""></span>mm<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>0.625<span class="elsevierStyleHsp" style=""></span>mm, measured field of view (FOV) of 50<span class="elsevierStyleHsp" style=""></span>cm, noise index of 20% and reconstructed to images of 0.625<span class="elsevierStyleHsp" style=""></span>mm transverse pixel size and 3.75<span class="elsevierStyleHsp" style=""></span>mm slice thickness. PET data was acquired in 3D mode with scan duration of 2<span class="elsevierStyleHsp" style=""></span>min per bed position and an axial FOV of 153<span class="elsevierStyleHsp" style=""></span>mm. The emission data was corrected in a standardized way (random, scatter and attenuation) and iteratively reconstructed (matrix size 256<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>256, Fourier rebinning, VUE Point FX [3D] with 3 iterations, 18 subsets).</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Visual and quantitative interpretation</span><p id="par0030" class="elsevierStylePara elsevierViewall">Quantitative PET/CT parameters used in the study were calculated according to a standard protocol on a dedicated workstation (Volumetrix for PET-CT and AW volume share 4.5, GE Healthcare, Waukesha, WI, USA). SUVmax and SUVmean corrected for body weight were computed by standard methods from the activity at the most intense voxel in the three-dimensional tumour region from the transaxial whole body images on attenuation-corrected PET/CT images. MTV (cm<span class="elsevierStyleSup">3</span>) was measured with semiautomatic PET analysis software using an automatic isocontour threshold method based on a theory of being greater than 42% of the SUV max value within the tumour. TLG values were calculated by multiplying MTV and SUV mean. The corresponding CT scan of lesions were used as a guideline to demarcate them if their boundaries were difficult to define for the calculation of SUVmax.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Statistical analysis</span><p id="par0035" class="elsevierStylePara elsevierViewall">The whole data were analyzed by IBM Corp. released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. Number and percentage values were used for the description of categorical data; mean, median, standart deviation (SD), minimum (min) and maximum (max) values were used for the description of continuous data. In the comparisons between groups, Chi-Square or Fisher's exact test were used for categorical variables, Mann–Whitney <span class="elsevierStyleItalic">U</span> test for continuous variables. The variables having a value of <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05 were accepted as statistically significant. ROC curve analysis was performed to evaluate the predictive value of TLG on overall survival and cutoff levels were determined. Sensitivity and specificity were calculated according to cutoff values. Kaplan–Meier test was used for survival analysis. The study was approved by our Institutional Review Board Committee.</p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Results</span><p id="par0040" class="elsevierStylePara elsevierViewall">Mean age of the patients was 52<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13 years (31–73). 36% of the cases were male, 64% female (male/female ratio: 0.56). Mean serum LDH level was 381<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>172<span class="elsevierStyleHsp" style=""></span>U/L (normal range 120–250<span class="elsevierStyleHsp" style=""></span>U/L), mean SUVmax value 19<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>7.5 (9.8–30.8), average SUVmean 10.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>3.8 (6.5–19.3), mean MTV 49.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>12.6<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> (10–183), mean TLG 526<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>136 (80.3–1628). Mean OS and DFS was 19.9<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>14 months (3–58), 9.85<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>6 months (3–28), respectively. There was deep brain involvement in 64% of the patients. Nine patients (64%) died, 12 patients (86%) developed recurrence and/or metastases (met/rec) during the follow-up. Patient characteristics and demography, clinicopathologic features and follow-up data are illustrated in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. Overall survival at 1st year was 79%, 50% at 2nd year, 36% at 5th year. Average period of met/rec was 8.3 months (3–18). DFS at first year was 28.5%, 14% at second year.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0045" class="elsevierStylePara elsevierViewall">After the comparison all potential risk factors (sex, age, serum LDH level, existence of deep brain involvement, SUVmax, SUVmean, MTV, TLG, recurrence) impacting DFS and OS by statistical analysis, we found only TLG (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.02) as a significant parameter. A box-plot graph illustrates the distribution of TLG in the patients according to survival groups (live versus death cases) (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). The results of statistical evaluation of risk factors were represented in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. ROC curve was drawn to evaluate the diagnostic value TLG (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). Cut-off values, sensitivity and specificity of TLG were demonstrated in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>. Kaplan–Meier method with log-rank test was used to compare DFS and OS of groups. Kaplan–Meier curve was plotted for TLG with a cutoff value of 416 (<a class="elsevierStyleCrossRefs" href="#fig0015">Figs. 3 and 4</a>).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Discussion</span><p id="par0050" class="elsevierStylePara elsevierViewall">Although the treatment of primary CNS lymphomas has become quite improved lately, current dependable parameters of large-seried, population-based validity for the follow-up of treatment response are still necessary. A suitable predictive model which can fullfill this deficiency would be very beneficial in the guideline for the maintenance of proper therapy with reproducible results. Age and Karnofsky performance status (KPS) were found as main independent variables in most of the studies.<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">4</span></a> Three prognostic scoring systems based on an aggregate number of adverse factors have been suggested lately for PCNSL.<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">4</span></a> First, the Nottingam/Barcelona scale derived from 77 consecutive patients issues age, KPS, multifocality and meningeal disease,<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">8</span></a> but its scoring power is limited. Second, the International Extranodal Lymphoma Study Group (IELSG) proposed a five-point scoring scale composed of age, KPS, serum LDH level, CSF total protein cencentration and deep brain involvement and it was derived from retrospective analysis of 378 patients.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">9</span></a> Third, the Memorial Sloan-Kettering Cancer Center (MSKCC) prognostic modeling used analytic technique of recursive partitioning analysis in 338 patients (during past 20 years) with PCNSL to develop predictive scores.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">10</span></a></p><p id="par0055" class="elsevierStylePara elsevierViewall">Magnetic resonance (MR) imaging has an important role in diagnosis and therapy monitoring of CNS lymphoma.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">11</span></a> Although MR is the choice of imaging modality owing to the presence of dense physiologic FDG activity in the cerebral cortex hindering the visualization of lesions, FDG-PET/CT is now well established in the evaluation of CNS lymphomas with a pattern of intense FDG uptake.<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">12</span></a> FDG-PET/CT is superior to and more valuable than MR in whole-body screening to show an involvement outside CNS and differentiating residuel masses from tumour tissue. It is hard to achieve a durable response in primary CNS lymphoma. Up to 60% of the patients who obtain a complete remission will relapse in the first two years<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">13</span></a> and at least 5% of cases metastasize outside CNS.<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">14</span></a> 90% of our cases relapsed in the first two years, besides recurrence occurred during the first year in 60% of them. Two patients developed extracranial metastases. A solitary bone metastasis was detected in one patient and systemic involvement in another one (This patient was a male aged 45 years and died 3 months later).</p><p id="par0060" class="elsevierStylePara elsevierViewall">High FDG uptake in tumours means high proliferative activity and is associated with a poor patient outcome.<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">15</span></a> However, FDG uptake in aggressive NHL is usually quite variable and overlaps partially.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">1</span></a> This gives rise to the question that to what extent FDG-PET might be useful in assessing patients according to better or worse clinical course and prognosis. FDG-PET is used at initial staging, restaging to detect recurrence and evaluation of treatment response in primary CNS lymphoma.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">16</span></a> However, the use of metabolic parameters on baseline FDG-PET except SUVmax for the management of primary CNS lymphoma has not been comprehensively investigated until now. Recently, Kawai et al. reported that a high SUVmax value was related with a decreased DFS and OS in univariate analysis.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">17</span></a> But the number of primary CNS lymphoma patients in their study was small (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>17). Kasenda et al. investigated the potential predictive value of pretreatment FDG uptake regarding to tumour response, DFS and OS with SUVmax as a marker of clinical aggressiveness to visually evaluate primary CNS lymphoma metabolism.<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">18</span></a> Only a few studies with limited number of patients have been published in the literature on the use of initial FDG-PET/CT in the management of these patients. Moreover there is not a study about the other metabolic tumour parameters in this selective subgroup of lymphoma. This study was conducted to investigate the usefulness of all metabolic tumour indexes in this area. To the best of our knowledge, our study is the first one in which prognosis and outcome of primary CNS lymphoma was estimated over metabolic tumour parameters. The inverse correlation between high FDG uptake and survival in our study, as parallel to the notion that lymphoma aggressiveness is usually associated with high FDG uptake, implies that pretreatment FDG-PET is a potential predictor in the management and outcome of primary CNS lymphoma. We also compared them with the classic adverse factors used in current validated scoring systems except KPS and CSF protein concentration. After univariate analysis, we found that TLG remained the most significant one amongst them (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.02). TLG, a volume-based metabolic tumour parameter has been gaining increasing recognition in many cancers and several lymphoma types as a predictor recently. The utility of TLG was stated in a lot of studies. Ozmen et al. reported that TLG contributed to patient management in Pancoast tumours.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">19</span></a> Zhou et al. declared that baseline TLG is the only independent predictor for DFS and OS in DLBCL patients treated with R-CHOP therapy.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">20</span></a> Song et al. claimed high TLG on FDG-PET/CT is the most significant predictor of CNS relapse in untreated DLBCL patients.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">21</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">Age and a KPS of 70% or more were accepted as the strongest predictors of OS in literature.<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">l,4</span></a> Age over 60 years is considered a bad predictor and age under 50 years a good one.<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">1,4</span></a> But 5/7 (70%) of the patients aged under 50 years in our study died and this is a controversial finding according to literature. Nevertheless, we could not compare KPS with metabolic parameters and this is the main limitation of our study. Another major limitation in our study is the small patient number as previous studies in literature. Because primary CNS lymphoma is rare, cases with primary staging FDG-PET and proper follow-up are even more rare. Ideally, a large multicenter study designed prospectively would be necessary to refine the current valid scores, risk factors and probably to combine them with the new predictors (especially TLG) derived from baseline FDG-PET.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Conclusion</span><p id="par0070" class="elsevierStylePara elsevierViewall">We propose that primary staging FDG-PET be included in baseline evaluation of primary CNS lymphoma not only for the exclusion of systemic disease but also for its prognostic role. Metabolic tumour parameters are useful in the prognostic estimation of primary CNS lymphomas. They may be used as a prognostic component in current scoring schemes. In particular, TLG is the most meaningful predictor and may play a role in patient management.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conflict of interest</span><p id="par0075" class="elsevierStylePara elsevierViewall">The authors declare no conflicts of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:3 [ "identificador" => "xres969145" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Material and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec940088" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres969146" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusión" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec940089" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "F-FDG-PET/CT imaging protocol" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Visual and quantitative interpretation" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0030" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0035" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0040" "titulo" => "Conclusion" ] 9 => array:2 [ "identificador" => "sec0045" "titulo" => "Conflict of interest" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2017-03-23" "fechaAceptado" => "2017-06-05" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec940088" "palabras" => array:3 [ 0 => "<span class="elsevierStyleSup">18</span>F-FDG-PET/CT" 1 => "Metabolic tumour parameters" 2 => "Primary CNS lymphoma" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec940089" "palabras" => array:3 [ 0 => "<span class="elsevierStyleSup">18</span>F-FDG-PET/TC" 1 => "Parámetros tumorales metabólicos" 2 => "Linfoma primario del SNC" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Primary central nervous system (CNS) lymphoma is an aggressive and fatal extranodal non-Hodgkin lymphoma (NHL) jailed in CNS at initial diagnosis. Its prognosis is poor and the disease has a fatal outcome when compared with systemic NHL. A few baseline risk stratification scoring systems have been suggested to estimate the prognosis mainly based on serum lactate dehydrogenase (LDH) level, age, Karnofsky performance score (KPS), involvement of deep brain structures (DBS) and cerebrospinal fluid protein concentration. <span class="elsevierStyleSup">18</span>F-FDG-PET/CT has a high prognostic value with respect to overall survival (OS) and disease-free survival (DFS) in many cancers and lymphomas. We aimed to investigate metabolic tumour indexes on primary staging <span class="elsevierStyleSup">18</span>F-FDG-PET/CT as prognostic markers in primary CNS lymphoma.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Material and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">14 patients with primary CNS diffuse large B-cell lymphoma (stage I) were enrolled in this retrospective cohort study. Primary staging <span class="elsevierStyleSup">18</span>F-FDG-PET/CT was performed and quantitative parameters like maximum standardized uptake value (SUVmax), average standardized uptake value (SUVmean), metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were calculated for all patients before the treatment. Cox regression models were performed to determine their relation with survival time.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">In the evaluation of all potential risk factors impacting recurrence/metastases (age, sex, serum LDH, involvement of DBS, SUVmax, SUVmean, MTV, TLG) with univariate analysis, TLG remained statistically significant (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.02).</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Metabolic tumour parameters are useful in prognosis estimation of primary CNS lymphomas, especially TLG, which is the most important one and may play a role in patient management.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Material and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Los linfomas del sistema nervioso central (SNC) son linfomas no Hodgkin extranodales, agresivos y fatales, albergados en el SNC en el diagnóstico inicial. Su pronóstico es malo, teniendo la enfermedad un resultado fatal en comparación con el linfoma no Hodgkin sistémico. Se han indicado una serie de sistemas de estratificación del riesgo para estimar el pronóstico, basados principalmente en el nivel sérico de lactato de deshidrogenasa, la edad, el índice de Karnofsky, el compromiso de las estructuras profundas del cerebro y la concentración proteica del líquido cefalorraquídeo. La PET/TC con <span class="elsevierStyleSup">18</span>F-FDG tiene un elevado valor pronóstico en cuanto a la supervivencia global y la supervivencia libre de enfermedad en muchos cánceres y linfomas. Nuestro objetivo fue investigar los índices tumorales metabólicos mediante <span class="elsevierStyleSup">18</span>F-FDG-PET/TC para estadificación primaria, como marcadores pronósticos de los linfomas primarios del SNC.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Material y métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se incluyó en este estudio retrospectivo a una cohorte de 14 pacientes con linfoma primario de células B grandes del SNC (estadio <span class="elsevierStyleSmallCaps">i</span>). Se realizó una PET/TC con <span class="elsevierStyleSup">18</span>F-FDG de estadificación primaria, calculándose en todos los pacientes, y con anterioridad al tratamiento, los parámetros cuantitativos tales como el valor estandarizado de captación máximo, el valor de captación medio, el volumen tumoral metabólico y la glucólisis tumoral total (TLG). Se realizaron modelos de regresión de Cox para determinar su relación con el tiempo de supervivencia.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">En la evaluación de todos los factores de riesgo potencial con impacto sobre la recidiva/metástasis (edad, sexo, lactato de deshidrogenasa sérico, compromiso de las estructuras profundas del cerebro, el valor estandarizado de captación máximo, el valor de captación medio, el volumen tumoral metabólico y la TLG), realizada mediante análisis univariante; el valor de TLG reflejó una significación estadística (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,02).</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusión</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Los parámetros tumorales metabólicos resultan de utilidad para la estimación pronóstica de los linfomas primarios del SNC. En especial, la TLG constituye el parámetro más importante, y puede desempeñar un papel en el tratamiento del paciente.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusión" ] ] ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1174 "Ancho" => 1583 "Tamanyo" => 48702 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Box-plot graph of TLG illustrating the distribution in patients according to live versus death cases.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1616 "Ancho" => 1583 "Tamanyo" => 101602 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">ROC curve of TLG showing its predictive value.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1841 "Ancho" => 1583 "Tamanyo" => 119040 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier curve for TLG associated with overall survival.</p>" ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1797 "Ancho" => 2167 "Tamanyo" => 207281 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">MIP image (A), transaxial PET (B) and transaxial fusion (C) images on baseline FDG-PET/CT of 66-year old female patient with primary central nervous system lymphoma reveal dense uptake in left parietal lobe (SUVmax 29.8, SUVmean 19.3, MTV 19.5<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>, TLG 375).</p>" ] ] 4 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Rec/Met: recurrence/metastasis, M: male, F: female.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Patient no \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Age \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Gender \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Serum LDH \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Presentation site \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Rec/Met \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Ex \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">SUV max \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">SUV mean \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">MTV \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">TLG \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">DFS \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">OS \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">378 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Corpus callosum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">43.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">456.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">58 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">31 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">M \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">725 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Occipital lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">236 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">M \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">358 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Frontoparietal lobe, cerebellum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">183 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1628.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">565 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Parietooccipital lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">217.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">134 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Cerebellum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">105 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">M \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">427 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Occipital lobe, cerebellum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">22.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">63.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">782.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">251 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Cerebellum, lateral ventricle \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">21.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">180.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">600 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Thalamus, parietal lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">118 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1569 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">250 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Hippocampus, medial temporal lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">200.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">M \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">293 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Temporal lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">179.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">223 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Parietal lobe, basal ganglia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">375 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">M \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">552 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Temporal lobe, thalamus \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">541.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">47 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">350 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Frontal lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">− \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">F \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">225 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Temporooccipital lobe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">+ \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">811.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1639729.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Patient characteristics and demography, clinicopathologic features and follow-up data.</p>" ] ] 5 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Factors \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> Value \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">SUVmax \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.423 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">SUVmean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.593 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">MTV (cm<span class="elsevierStyleSup">3</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.156 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">TLG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.02 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.461 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Serum LDH (U/L) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.205 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Deep brain involvement \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.266 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Sex (male or female) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.580 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Recurrence \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.110 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1639728.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Comparisons between predefined risk factors by Chi-Square or Fisher's exact for categorical variables, Mann–Whitney <span class="elsevierStyleItalic">U</span> test for continuous variables.</p>" ] ] 6 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Cutoff values \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Area under curve (%95 confidence interval) \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">416 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">67 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">100 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="3" align="char" valign="middle">0.889 (0.712–1.000)</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">227 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">78 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="char" valign="top">180 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">100 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">60 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1639727.png" ] ] ] ] "descripcion" => array:1 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Original article
Can metabolic tumour parameters on primary staging 18F-FDG-PET/CT contribute to risk stratification of primary central nervous system lymphomas for patient management as a prognostic model?
¿Pueden contribuir los parámetros tumorales metabólicos, establecidos mediante 18F-FDG-PET/TC para estadificación primaria, a la estratificación del riesgo de los linfomas del sistema nervioso central, para el tratamiento del paciente como modelo pronóstico?