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RICE-NET, a novel multimodal 3D deep learning model, was developed to differentiate between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients. The model integrates longitudinal MRI data with radiotherapy dose distributions using conventional T1-weighted MRI. Evaluated on a cohort of 92 patients, RICE-NET achieved a strong F1 score of 0.92 on an independent test set, demonstrating the importance of incorporating radiation maps for reliable classification.
A deep learning model can now distinguish between tumor recurrence and radiation effects in brain cancer patients with high accuracy, primarily by leveraging radiotherapy dose distributions alongside MRI data.
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.
