GLIOSURVNET: MULTIMODAL SURVIVAL PREDICTION FOR GLIOBLASTOMA USING DEEP LEARNING AND CLINICAL VARIABLES FROM BRAIN MRI

Accurate survival prediction using multimodal magnetic resonance imaging (MRI) plays a crucial role in clinical decision-making for patients with glioblastoma (GBM). In this work, we propose a multimodal framework, GlioSurvNet, that integrates deep learning features extracted from Swin UNETR and clinical variables to predict patient survival. Our framework makes use of multiple MRI sequences, including T1, T1 with contrast enhancement, T2-weighted, and FLAIR MRI, to capture diverse tumor characteristics. The Swin UNETR architecture simultaneously carries out tumor segmentation and extracts hierarchical features from multimodal MRI data. These deep learning features are then combined with clinical variables, which are input into a multi-layer perceptron network to yield survival probabilities. We evaluated our framework on a cohort of 287 patients from two independent databases, UPENN-GBM and UCSF-PDGM, demonstrating superior survival prediction performance when compared with existing methods. Our framework achieved a time-dependent concordance index of 0.693 and an integrated brier score of 0.14 with improved risk stratification. GlioSurvNet offers a robust tool for personalized prognosis and treatment planning in GBM patients.