Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.

ABSTRACT: Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles. xAI determined the prognostic contribution of each clinical marker at the patient level and identified 114 key markers that accounted for 90% of the neural network's decision process. Moreover, xAI enabled us to uncover 1,373 prognostic interactions between markers. Our approach was validated in a

SUBMITTER: Keyl J 

PROVIDER: S-EPMC11864985 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature