Perez-Beteta, JulianMolina-Garcia, DavidMartinez-Gonzalez, AliciaHenares-Molina, AraceliAmo-Salas, MarianoLuque, BelenArregui, ElenaCalvo, ManuelBorras, Jose MMartino, JuanVelasquez, CarlosMelendez-Asensio, Barbarade-Lope, Angel RodriguezMoreno, RaquelBarcia, Juan AAsenjo, BeatrizBenavides, ManuelHerruzo, IsmaelLara, Pedro CCabrera, RaquelAlbillo, DavidNavarro, MiguelPerez-Romasanta, Luis ARevert, AntonioArana, EstanislaoPerez-Garcia, Victor M2023-01-252023-01-252018-10-15Pérez-Beteta J, Molina-García D, Martínez-González A, Henares-Molina A, Amo-Salas M, Luque B, et al. Morphological MRI-based features provide pretreatment survival prediction in glioblastoma. Eur Radiol. 2019 Apr;29(4):1968-1977http://hdl.handle.net/10668/13310Objectives: 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.enGlioblastomaPrognosisBiomarkersSurvival analysisMultivariate analysisPrognosisLinear ModelsBiopsyMagnetic Resonance Imagingresearch article30547198Restricted AccessDescriptoresEspectroscopía de Resonancia MagnéticaNeoplasiasImagen por Resonancia MagnéticaModelos Lineales10.1007/s00330-018-5758-71432-1084https://link.springer.com/content/pdf/10.1007/s00330-018-5870-8.pdf