%0 Journal Article
%A Perez-Beteta, Julian
%A Molina-Garcia, David
%A Martinez-Gonzalez, Alicia
%A Henares-Molina, Araceli
%A Amo-Salas, Mariano
%A Luque, Belen
%A Arregui, Elena
%A Calvo, Manuel
%A Borras, Jose M
%A Martino, Juan
%A Velasquez, Carlos
%A Melendez-Asensio, Barbara
%A de-Lope, Angel Rodriguez
%A Moreno, Raquel
%A Barcia, Juan A
%A Asenjo, Beatriz
%A Benavides, Manuel
%A Herruzo, Ismael
%A Lara, Pedro C
%A Cabrera, Raquel
%A Albillo, David
%A Navarro, Miguel
%A Perez-Romasanta, Luis A
%A Revert, Antonio
%A Arana, Estanislao
%A Perez-Garcia, Victor M
%T Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.
%D 2018
%U http://hdl.handle.net/10668/13310
%X Objectives: We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients. Methods: A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell’s concordance indexes (c-indexes) were used for the statistical analysis. Results: A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87). Conclusions: Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures.
%K Glioblastoma
%K Prognosis
%K Biomarkers
%K Survival analysis
%K Multivariate analysis
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