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. |
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| 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 41720170 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Reuter+LM%22">Reuter LM</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Kraus+KM%22">Kraus KM</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Fischer+SM%22">Fischer SM</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Pletzer+D%22">Pletzer D</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Bernhardt+D%22">Bernhardt D</searchLink>; Department of Radiation Oncology, School of Medicine, TUM Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany.<br /><searchLink fieldCode="AU" term="%22Combs+SE%22">Combs SE</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Schnabel+JA%22">Schnabel JA</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Peeken+JC%22">Peeken JC</searchLink>; 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. – Name: AuthorCorporate Label: Corporate Authors Group: Au Data: <searchLink fieldCode="CA" term="%22REQUITE+consortium%22">REQUITE consortium</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%227603616%22">International journal of radiation oncology, biology, physics</searchLink> [Int J Radiat Oncol Biol Phys] 2026 Jul 15; Vol. 125 (4), pp. 1149-1159. <i>Date of Electronic Publication: </i>2026 Feb 18. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Multicenter Study – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%2C+Inc%22">Elsevier, Inc </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>7603616 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-355X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2203603016%22">03603016 </searchLink><i>NLM ISO Abbreviation: </i>Int J Radiat Oncol Biol Phys <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41720170 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ijrobp.2026.01.031 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 1149 Titles: – TitleFull: 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. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Reuter LM – PersonEntity: Name: NameFull: Kraus KM – PersonEntity: Name: NameFull: Fischer SM – PersonEntity: Name: NameFull: Pletzer D – PersonEntity: Name: NameFull: Bernhardt D – PersonEntity: Name: NameFull: Combs SE – PersonEntity: Name: NameFull: Schnabel JA – PersonEntity: Name: NameFull: Peeken JC IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: 2026 Jul 15 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1879-355X Numbering: – Type: volume Value: 125 – Type: issue Value: 4 Titles: – TitleFull: International journal of radiation oncology, biology, physics Type: main |
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