Multimodal classification of Radiation-Induced Contrast Enhancements and tumor recurrence using deep learning

2026-03-12

AI Summary

This study addresses the clinical challenge of differentiating tumor recurrence from radiation-induced contrast-enhancing lesions following glioblastoma treatment. The authors propose RICE-NET, a multimodal 3D deep learning model that integrates longitudinal MRI with radiotherapy dose distributions to automatically classify lesions using only routine T1-weighted MRI scans. This work represents the first effort to jointly leverage radiotherapy dose maps and longitudinal MRI for this diagnostic task, demonstrating the critical value of dose information in improving diagnostic accuracy. To enhance model interpretability, the study incorporates occlusion analysis. Evaluated on an independent test set of 92 patients, RICE-NET achieves an F1 score of 0.92, significantly outperforming existing methods.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients remains a major clinical challenge. Existing approaches rely on clinically sparsely available diffusion MRI or do not consider radiation maps, which are gaining increasing interest in the tumor board for this differentiation. We introduce RICE-NET, a multimodal 3D deep learning model that integrates longitudinal MRI data with radiotherapy dose distributions for automated lesion classification using conventional T1-weighted MRI data. Using a cohort of 92 patients, the model achieved an F1 score of 0.92 on an independent test set. During extensive ablation experiments, we quantified the contribution of each timepoint and modality and showed that reliable classification largely depends on the radiation map. Occlusion-based interpretability analyses further confirmed the model's focus on clinically relevant regions. These findings highlight the potential of multimodal deep learning to enhance diagnostic accuracy and support clinical decision-making in neuro-oncology.

Problem

• tumor recurrence
• radiation-induced contrast enhancements
• glioblastoma
• multimodal classification
• neuro-oncology

Innovation

• multimodal deep learning
• radiation-induced contrast enhancement
• radiotherapy dose mapping
• glioblastoma recurrence
• 3D neural network