Explainable AI Predicts Hematoxicity from Cancer Treatment Using Multimodal Real-World Data

作者：Julius Keyl, Philipp Keyl, Tim Lenfers, René Hosch, Niklas Kiermeyer, Simon Schallenberg, Moon Kim, Sebastian Bauer, Nikolaos Bechrakis, Michael Forsting, Dagmar Fuehrer-Sakel, Sied Kebir, Viktor Gruenwald, Boris Hadaschik, J Haubold, K Herrmann, Stefan Kasper, Rainer Kimmig, Stephan Lang, Tienush Rassaf, Alexander Roesch, Dirk Schadendorf, Jens T. Siveke, Martin Stuschke, U Sure, Matthias Totzeck, Anja Welt, Marcel Wiesweg, Jan Egger, Sylvia Hartmann, Grégoire Montavon, Felix Nensa, Klaus-Robert Mueller, Martin Schuler, J Kleesiek, F Klauschen · 发表于：medRxiv · 年份：2026 · DOI：10.64898/2026.04.29.26352032 · 被引用次数：1 · 研究领域：Explainable Artificial Intelligence (XAI)、Radiomics and Machine Learning in Medical Imaging、Biomarkers in Disease Mechanisms

Abstract Adverse drug effects remain a major barrier to safe and effective cancer therapy, underscoring the need for tools that predict treatment-related toxicities. We analyzed multimodal real-world data from 14,596 cancer patients across 38 cancer entities, encompassing 330 clinical, tumor, and imaging characteristics, along with 89 anticancer agents. Hematological adverse events (HAE), defined by nadirs of hemoglobin, leukocyte, neutrophil, and platelet values within two months of treatment initiation, were highly prevalent (87.7%; 33.1% severe). We developed Toxix , an explainable artificial intelligence (xAI) framework modeling interactions between patient characteristics and drug combinations. Toxix achieved strong predictive performance for severe toxicities (median AUROC 0.85 for anemia; >0.76 for leukopenia, neutropenia, and thrombocytopenia) and was validated in an external cohort of 2,768 patients with non-small cell lung cancer. Model explainability enabled systematic characterization of drug-patient interactions underlying HAEs. Toxix provides a real-world informed framework for personalized and toxicity-aware cancer therapy planning.