Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence
Essen University Hospital · Berlin Institute for the Foundations of Learning and Data · +14 more institutions
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…
Citation impact
- FWCI
- 50.78
- Percentile
- 100%
- References
- 58
Authors
38- JKJulius Keyl
Essen University Hospital
- PKPhilipp Keyl
Berlin Institute for the Foundations of Learning and Data, Ludwig-Maximilians-Universität München
- GMGrégoire Montavon
Berlin Institute for the Foundations of Learning and Data, Technische Universität Berlin, Freie Universität Berlin
- RHRené Hosch
Essen University Hospital
- ABAlexander Brehmer
Essen University Hospital
Topics & keywords
- Artificial intelligence
- Computer science
- Cancer
- Cohort
- Artificial neural network
- Medicine
- Internal medicine