Morphological MRI-based features provide pretreatment survival prediction in glioblastoma View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

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

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. More... »

PAGES

1968-1977

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-018-5758-7

DOI

http://dx.doi.org/10.1007/s00330-018-5758-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107638656

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30324390


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