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European Radiology
Published in: European Radiology 4/2019

01-04-2019 | Head and Neck

Morphological MRI-based features provide pretreatment survival prediction in glioblastoma

Authors: Julián Pérez-Beteta, David Molina-García, Alicia Martínez-González, Araceli Henares-Molina, Mariano Amo-Salas, Belén Luque, Elena Arregui, Manuel Calvo, José M. Borrás, Juan Martino, Carlos Velásquez, Bárbara Meléndez-Asensio, Ángel Rodríguez de Lope, Raquel Moreno, Juan A. Barcia, Beatriz Asenjo, Manuel Benavides, Ismael Herruzo, Pedro C. Lara, Raquel Cabrera, David Albillo, Miguel Navarro, Luis A. Pérez-Romasanta, Antonio Revert, Estanislao Arana, Víctor M. Pérez-García

Published in: European Radiology | Issue 4/2019

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Abstract

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.

Key Points

• A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients’ age outperformed previous prognosis scores for glioblastoma.
• Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.
Appendix
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Metadata
Title
Morphological MRI-based features provide pretreatment survival prediction in glioblastoma
Authors
Julián Pérez-Beteta
David Molina-García
Alicia Martínez-González
Araceli Henares-Molina
Mariano Amo-Salas
Belén Luque
Elena Arregui
Manuel Calvo
José M. Borrás
Juan Martino
Carlos Velásquez
Bárbara Meléndez-Asensio
Ángel Rodríguez de Lope
Raquel Moreno
Juan A. Barcia
Beatriz Asenjo
Manuel Benavides
Ismael Herruzo
Pedro C. Lara
Raquel Cabrera
David Albillo
Miguel Navarro
Luis A. Pérez-Romasanta
Antonio Revert
Estanislao Arana
Víctor M. Pérez-García
Publication date
01-04-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 4/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5758-7

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