Hybrid Neural–Bayesian Belief Network Framework for Uncertainty-Aware Multimodal GBM Prediction

Abstract excerpt

<h4>Background and Objective</h4> Glioblastoma outcome prediction remains difficult because clinically relevant signals are distributed across heterogeneous imaging and genomic modalities, cohorts are small, and conventional neural predictors do not quantify their own uncertainty. This study evaluates a hybrid neural–Bayesian belief network framework for uncertainty-aware multimodal glioblastoma prediction and ex...

Topics

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• Glioma Diagnosis and Treatment
• Radiomics and Machine Learning in Medical Imaging

Identifiers and source

Literature Corpus work

b83b8de2-a622-5a5e-a5d4-63215e93b40b

DOI

10.64898/2026.05.10.26352710