Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles

Abstract

BackgroundMagnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy. PurposeTo evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients. Materials and methodsMulti-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983–2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS. ResultsThe average age of patients was 51.2years (women: n = 77, age-range=18–84years; men: n = 83, age-range=21–80years). The median OS of the participants was 494.5 (range,3–4752), 481 (range,7–4752), and 524.5 days (range,3–2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87). ConclusionThe combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.