Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.
Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy.
Author(s): Wang, Zijun, Liu, Xi, Han, Kaitai, Lei, Lixin, Shi, Chaojing, Liu, Wu, Guo, Qianjin
DOI: 10.1093/jamia/ocaf142