AUTHORS: Bodenmann T.R., Gil N., Dorfner F.J., Cleveland M.C., Patel J.B., Bhat Brahmavar S., Guelen M.S., Pulido-Arias D., Kalpathy-Cramer J., Thiran J.-P., Rosen B.R., Gerstner E., Kim A. E., Bridge C. P.
MICCAI Workshop on Cancer Prevention through Early Detection, : , Marrakesh, 09 October 2024
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.
BibTex
https://doi.org/10.1007/978-3-031-73376-5_4
Module: SP
