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

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 R. Gerstner, Albert E. Kim, Christopher P. Bridge

Abstract: 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.

External IDs:dblp:conf/miccai/BodenmannGDCPBGPKTRGKB24