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The differentiation between tumor recurrence and radiationinduced 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 a performance of 0.92 [F1] 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.

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Multimodal Classification of Radiation-induced Contrast Enhancement and Tumor Recurrence using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Robin Peretzke,
  • Marlin Hanstein,
  • Maximilian Fischer,
  • Lars Wessel,
  • Obada Alhalabi,
  • Sebastian Regnery,
  • Andreas Kudak,
  • Maximilian Deng,
  • Tanja Eichkorn,
  • Philipp Hoegen-Saßmannshausen,
  • Fabian Allmendinger,
  • Jan-Hendrik Bolten,
  • Philipp Schröter,
  • Christine Jungk,
  • Jürgen Debus,
  • Peter Neher,
  • Laila König,
  • Klaus Maier-Hein

摘要

The differentiation between tumor recurrence and radiationinduced 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 a performance of 0.92 [F1] 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.