Author:
Keyl Julius,Keyl Philipp,Montavon Grégoire,Hosch René,Brehmer Alexander,Mochmann Liliana,Jurmeister Philipp,Dernbach Gabriel,Kim Moon,Koitka Sven,Bauer Sebastian,Bechrakis Nikolaos,Forsting Michael,Führer-Sakel Dagmar,Glas Martin,Grünwald Viktor,Hadaschik Boris,Haubold Johannes,Herrmann Ken,Kasper Stefan,Kimmig Rainer,Lang Stephan,Rassaf Tienush,Roesch Alexander,Schadendorf Dirk,Siveke Jens T.,Stuschke Martin,Sure Ulrich,Totzeck Matthias,Welt Anja,Wiesweg Marcel,Baba Hideo A.,Nensa Felix,Egger Jan,Müller Klaus-Robert,Schuler Martin,Klauschen Frederick,Kleesiek Jens
Abstract
AbstractDespite advances in precision oncology, clinical decision-making still relies on limited parameters and expert knowledge. To address this limitation, we combined multimodal real- world data and explainable artificial intelligence (xAI) to introduce novel AI-derived (AID) markers for clinical decision support.We used deep learning to model 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 lung cancer patients from a US nationwide electronic health record-derived database.These results show the potential of xAI to transform the assessment of clinical parameters and enable personalized, data-driven cancer care.
Publisher
Cold Spring Harbor Laboratory