A Subregion-based RadioFusionOmics Model Discriminates between Grade 4 Astrocytoma and Glioblastoma on Multisequence MRI

Motivation: Investigated the underlying impact of subregional analysis on model performance: comparison of two volumes of interests (VOI) definition strategies. Goal(s): To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma. Approach: Subregional radiomics analysis using the K-means clustering demonstrated discriminative performance comparable to that of manual segmentation. Edematous subregion is a possible intratumoral heterogeneity phenotype that differentiates grade 4 astrocytoma from glioblastoma. Results: The RFO model that was trained using fused features achieved the AUC of 0.868 (VOI3) and 0.884 (H34) in the primary cohort (p=0.059), and 0.824 (VOI3) and 0.838 (H34) in the testing cohort (p=0.023). Impact: Fusion of features from edematous subregions of multiple MRI sequences by the RFO model identified IDH genotypes of adult type grade 4 gliomas in line with current WHO CNS 5 criteria.