Morphological MRI-based features provide pretreatment and post-surgery survival prediction in glioblastoma
J. Pérez-Beteta, D. Molina-García, A. Martínez-González, M. Amo, A. Henares-Molina, B. Luque, E. Arregui, M. Calvo, J.M.Borrás, J. Martino, C. Velasquez, B. Meléndez, A. R. de Lope, R. Moreno, J. A. Barcia, B. Asenjo, M. Benavides, I. Herruzo, P. C. Lara, R. Cabrera, D. Albillo, M. Navarro, L. A. Pérez-Romasanta, A. Revert, E. Arana, V. M. Pérez-García
European Radiology 29(4) 1968-1977 (2019)
Pérez García, Victor M.. Martínez González, Alicia. Molina García, David. Pérez Beteta, Julián. Henares Molina, Araceli. Albillo Labarra, José David. Arana, Estanislao. Asenjo Martínez, Beatriz. Luque, Belén. Meléndez-Asensio, Bárbara. Pérez Romasanta, Luis. Herruzo, Ismael.
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 discovery and 93 validation) 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-enhancing (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. Our approach substantially outperformed more complex approaches including some based on machine-learning methods.