Morphological MRI-based features provide pretreatment survival prediction in glioblastoma
We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients.
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.
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).
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.
• 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.
KeywordsGlioblastoma Prognosis Biomarkers Survival analysis Multivariate analysis
Digital imaging and communication in medicine
Morphology- and age-based
Morphology- and age-based prognosis score for biopsied patients
Morphology- and age-based prognosis score for subtotally resected patients
Morphology- and age-based prognosis score for totally resected patients
Magnetic resonance images
The Cancer Image Archive
World Health Organization
We would like to thank C. López (Radiology Department, Hospital General de Ciudad Real), M. Claramonte (Neurosurgery Department, Hospital General de Ciudad Real), L. Iglesias (Neurosurgery Department, Hospital Clínico San Carlos), J. Avecillas (Radiology Department, Hospital Clínico San Carlos), J. M. Villanueva (Medical Oncology Department, Hospital Universitario de Salamanca), and J. C. Paniagua (Medical Oncology Department, Hospital Universitario de Salamanca) for their help in the data collection. We would also like to thank J. A. Ortiz Alhambra (Mathematical Oncology Laboratory) and A. Fernández-Romero (Mathematical Oncology Laboratory) for their help in the tumor segmentation tasks.
This research has been supported by the Ministerio de Economía y Competitividad/FEDER, Spain (grant number MTM2015-71200-R), and James S. Mc. Donnell Foundation Twenty-First Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (Collaborative award 220020450).
Compliance with ethical standards
The scientific guarantor of this publication is Victor Manuel Pérez-García, full professor and head of Department of Mathematics at Universidad de Castilla-La Mancha (Spain).
Conflict of interest
The authors declare that they have no conflict of interest.
Statistics and biometry
Complex statistical methods were necessary for this paper. However, Victor M. Pérez-García, Alicia Martínez-González, and David Molina (mathematicians) have significant statistical expertise.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• Multicenter study
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