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Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.

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Title: Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.
Authors: Reuter LM; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany. Electronic address: lukas.reuter@tum.de., Kraus KM; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany; Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, German Cancer Consortium (DKTK), Munich, Germany; Institute for Advanced Study, Technical University Munich, Garching, Germany., Fischer SM; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany., Pletzer D; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany., Bernhardt D; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany., Combs SE; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany; Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, German Cancer Consortium (DKTK), Munich, Germany., Schnabel JA; Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom., Peeken JC; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, Neuherberg, Germany; Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, German Cancer Consortium (DKTK), Munich, Germany.
Corporate Authors: REQUITE consortium
Source: International journal of radiation oncology, biology, physics [Int J Radiat Oncol Biol Phys] 2026 Jul 15; Vol. 125 (4), pp. 1149-1159. Date of Electronic Publication: 2026 Feb 18.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Elsevier, Inc Country of Publication: United States NLM ID: 7603616 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-355X (Electronic) Linking ISSN: 03603016 NLM ISO Abbreviation: Int J Radiat Oncol Biol Phys Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-355X
DOI:10.1016/j.ijrobp.2026.01.031