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

IF 23.5 1区医学 Q1 ONCOLOGY Nature cancer Pub Date : 2025-01-30 DOI:10.1038/s43018-024-00891-1

Julius Keyl, Philipp Keyl, Grégoire Montavon, René Hosch, Alexander Brehmer, Liliana Mochmann, Philipp Jurmeister, Gabriel Dernbach, Moon Kim, Sven Koitka, Sebastian Bauer, Nikolaos Bechrakis, Michael Forsting, Dagmar Führer-Sakel, Martin Glas, Viktor Grünwald, Boris Hadaschik, Johannes Haubold, Ken Herrmann, Stefan Kasper, Rainer Kimmig, Stephan Lang, Tienush Rassaf, Alexander Roesch, Dirk Schadendorf, Jens T Siveke, Martin Stuschke, Ulrich Sure, Matthias Totzeck, Anja Welt, Marcel Wiesweg, Hideo A Baba, Felix Nensa, Jan Egger, Klaus-Robert Müller, Martin Schuler, Frederick Klauschen, Jens Kleesiek

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引用次数: 0

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 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.

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来源期刊

Nature cancer Medicine-Oncology

CiteScore

31.10

自引率

1.80%

发文量

129

期刊介绍： Cancer is a devastating disease responsible for millions of deaths worldwide. However, many of these deaths could be prevented with improved prevention and treatment strategies. To achieve this, it is crucial to focus on accurate diagnosis, effective treatment methods, and understanding the socioeconomic factors that influence cancer rates. Nature Cancer aims to serve as a unique platform for sharing the latest advancements in cancer research across various scientific fields, encompassing life sciences, physical sciences, applied sciences, and social sciences. The journal is particularly interested in fundamental research that enhances our understanding of tumor development and progression, as well as research that translates this knowledge into clinical applications through innovative diagnostic and therapeutic approaches. Additionally, Nature Cancer welcomes clinical studies that inform cancer diagnosis, treatment, and prevention, along with contributions exploring the societal impact of cancer on a global scale. In addition to publishing original research, Nature Cancer will feature Comments, Reviews, News & Views, Features, and Correspondence that hold significant value for the diverse field of cancer research.