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

Menée à partir de données multimodales de vie réelle portant sur 15 726 patients atteints d'un cancer (38 types de tumeurs solides) et validée à partir de données de registres médicaux portant sur 3 288 patients atteints d'un cancer du poumon, cette étude met en évidence l'intérêt d'une approche utilisant l'intelligence artificielle pour décoder les résultats thérapeutiques, identifier des biomarqueurs et personnaliser les traitements

Résumé en anglais

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 an independent cohort of 3,288 patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care.