Tumor Recurrence or Treatment Effect? Large Multi-Institutional Evaluation of an Ai Risk Assessment Model in Glioma and Brain Metastases
Recommended Citation
Battalapalli D, Um H, Manjila S, Xiang D, Ismail M, Hill V, Puri S, Yu JS, Lu L, Nayate A, Higinbotham A, Rogers LR, Basree MM, Baschnagel A, McMillan A, Bhatia A, Singh Ahluwalia M, Veronesi M, Shi W, Tiwari P. Tumor Recurrence or Treatment Effect? Large Multi-Institutional Evaluation of an Ai Risk Assessment Model in Glioma and Brain Metastases. J Clin Oncol 2026; 44(16_Suppl).
Document Type
Conference Proceeding
Publication Date
5-27-2026
Publication Title
J Clin Oncol
Keywords
adult, brain metastasis, classifier, conference abstract, cross validation, deep learning, entropy, glioma, human, major clinical study, male, MRI scanner, nuclear magnetic resonance imaging, radiation necrosis, radiomics, random forest, retrospective study, risk assessment, risk model, T1 weighted imaging, therapy effect, tumor recurrence
Abstract
2080 Background: Distinguishing true tumor recurrence (TuR) from radiation necrosis (RN) on post-treatment MRI scans remains a major neuro-oncology challenge. We hypothesized that an integrated Spatial, Morphologic, and Textural radiomics risk (SMART-risk) model that comprehensively captures local and spatial organization of lesion heterogeneity, can unravel distinct biology across TuR and RN, on clinical MRI; and improve distinction over a data-driven ResNet50 deep learning model. Methods: We retrospectively collected multi-institutional post-treatment MRI cohorts: brain metastases (233 studies; from Cleveland Clinic (CCF), University Hospitals Cleveland (UH), University of Wisconsin (UW); and glioma (340 studies from Indiana University (IU), Dana-Farber Cancer Center, Thomas Jefferson University (TJU), and CCF). Over 80% of the studies were pathologically confirmed as TuR or RN. Institution-held-out external testing was performed (metastases: train CCF+UH, test UW; glioma: train IU+TJU+Dana-Farber Cancer Center, test CCF). Following segmentation, 944 radiomic features/lesion were extracted including graph-based spatial organization of tumor heterogeneity (GrRAiL), local gradient texture heterogeneity (COLLAGE), Haralick, and morphology. LASSO-selected features were integrated in a Random Forest classifier. Performance metrics included cross validation accuracy (CV), test accuracy, F1 score, and AUC; interpretability used SHAP. Comparison was performed with a ResNet50 baseline model. Results: SMART-Risk demonstrated consistent discrimination on the institution-held-out external test set (Table 1) with ~80% accuracy; ~10-15% improvement over a ResNet50 model. SHAP analysis indicated that features corresponding to spatial organization and local heterogeneity, i.e. average path length, node count and entropy measures, were dominant contributors (Mann–Whitney U test, p ≤ 0.001). Recurrent tumors showed higher spatial complexity and greater heterogeneity than RN. Conclusions: SMART-Risk may provide a noninvasive approach to distinguish TuR from RN on routine post-contrast T1-weighted MRI. By integrating spatial-organization (GrRAiL), texture (COLLAGE/Haralick), and morphology features, SMART-Risk captures complementary signatures and may improve discrimination compared with any single feature family. An AI-based SMART-Risk approach may reduce diagnostic ambiguity, support earlier treatment decisions, and avoid unnecessary invasive procedures. Table Presented
Volume
44
Issue
16_Suppl
