RT Journal Article
T1 Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.
A1 Perez-Beteta, Julian
A1 Molina-Garcia, David
A1 Martinez-Gonzalez, Alicia
A1 Henares-Molina, Araceli
A1 Amo-Salas, Mariano
A1 Luque, Belen
A1 Arregui, Elena
A1 Calvo, Manuel
A1 Borras, Jose M
A1 Martino, Juan
A1 Velasquez, Carlos
A1 Melendez-Asensio, Barbara
A1 de-Lope, Angel Rodriguez
A1 Moreno, Raquel
A1 Barcia, Juan A
A1 Asenjo, Beatriz
A1 Benavides, Manuel
A1 Herruzo, Ismael
A1 Lara, Pedro C
A1 Cabrera, Raquel
A1 Albillo, David
A1 Navarro, Miguel
A1 Perez-Romasanta, Luis A
A1 Revert, Antonio
A1 Arana, Estanislao
A1 Perez-Garcia, Victor M
K1 Glioblastoma
K1 Prognosis
K1 Biomarkers
K1 Survival analysis
K1 Multivariate analysis
AB 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.
PB Springer
YR 2018
FD 2018-10-15
LK http://hdl.handle.net/10668/13310
UL http://hdl.handle.net/10668/13310
LA en
NO Pérez-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):1968-1977
DS RISalud
RD Apr 6, 2025