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Recent studies demonstrate promising efficacy with immune checkpoint inhibitors (ICI) for brain metastases (BM), an unmet need in modern oncology. However, a predictive biomarker for ICI efficacy is needed to inform precision-based use of ICI given its high toxicity rate. Here, we present several multimodal deep learning (DL) approaches that integrate pre-treatment magnetic resonance imaging (MRI) and clinical metadata to predict ICI efficacy for BM. Using a multi-institutional dataset of 548 patients, our best-performing models achieve an AUROC of 0.674 (±0.041). In future work, we will accrue additional clinical and radiologic data to improve performance. Furthermore, our work thus far will serve as a baseline by which to trial alternate fusion strategies to improve and refine multimodal biomarker discovery for precision oncology.

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Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases

  • 会议论文

  • 发表日期：

    2025年

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  • Tobias R. Bodenmann,
  • Nelson Gil,
  • Felix J. Dorfner,
  • Mason C. Cleveland,
  • Jay B. Patel,
  • Shreyas Bhat Brahmavar,
  • Melisa S. Guelen,
  • Dagoberto Pulido-Arias,
  • Jayashree Kalpathy-Cramer,
  • Jean-Philippe Thiran,
  • Bruce R. Rosen,
  • Elizabeth Gerstner,
  • Albert E. Kim,
  • Christopher P. Bridge

摘要

Recent studies demonstrate promising efficacy with immune checkpoint inhibitors (ICI) for brain metastases (BM), an unmet need in modern oncology. However, a predictive biomarker for ICI efficacy is needed to inform precision-based use of ICI given its high toxicity rate. Here, we present several multimodal deep learning (DL) approaches that integrate pre-treatment magnetic resonance imaging (MRI) and clinical metadata to predict ICI efficacy for BM. Using a multi-institutional dataset of 548 patients, our best-performing models achieve an AUROC of 0.674 (±0.041). In future work, we will accrue additional clinical and radiologic data to improve performance. Furthermore, our work thus far will serve as a baseline by which to trial alternate fusion strategies to improve and refine multimodal biomarker discovery for precision oncology.