Work

Graph-radiomic learning (GrRAiL) descriptor to characterize imaging heterogeneity in confounding tumor pathologies

Year: 2026

Type: article

Abstract: A significant challenge in solid tumors is the reliable distinction of confounding pathologies from malignant neoplasms on routine imaging scans. While radiomics approaches have attempted to capture surrogate markers of lesion heterogeneity on clinical imaging (CT, MRI), these approaches often aggregate feature values across the region of interest (ROI) and may not capture the complex spatial relationships across various intensity compositions in the ROI. We present a new Graph-Radiomic Learning (GrRAiL) descriptor for characterizing intralesional heterogeneity (ILH) on clinical MRI scans. GrRAiL involves (1) identifying clusters of sub-regions based on per-voxel radiomic measurements, followed by (2) computing graph-theoretic measurements that analyze the spatial associations of radiomic clusters. The weighted graphs constructed via the GrRAiL descriptor capture the complexity of the higher-order spatial relationships within the ROI, in an attempt to reliably characterize ILH and distinguish confounding pathologies from malignant neoplasms. To evaluate its efficacy and clinical feasibility, GrRAiL descriptor was evaluated on a total of n=947 subjects spanning 3 different clinical use-cases including distinguishing radiation-effects from tumor recurrence in glioblastoma (n = 106) and brain metastasis (n = 233), as well as distinguishing no+low- versus high-risk pancreatic intraductal papillary mucus neoplasms (IPMNs) (n = 608). In a multi-institutional setting, GrRAiL consistently outperformed state-of-the-art approaches (Graph Neural Networks (GNNs), textural radiomics, and intensity graph analysis). For the glioblastoma use-case, the cross validation (CV) and test accuracies obtained to classify tumor recurrence from pseudo-progression, were 89% and 78%, respectively, with >10% improvement in test accuracy over comparative approaches. In the brain metastasis cohort, GrRAiL achieved CV and test accuracies of 84% and 74% in classifying tumor recurrence from radiation necrosis (>13% improvement over other approaches). Finally, when GrRAiL was used to classify between no+low- and high-risk IPMNs, CV and test accuracies of 85% and 79% were obtained, demonstrating >10% improvement over comparative approaches.

Source: npj Precision Oncology

DOI: 10.1038/s41698-026-01649-x