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Reuter, L. ; Kraus, K.M. ; Fischer, S.M. ; Pletzer, D. ; Bernhardt, D.* ; Combs, S.E. ; Schnabel, J.A. ; Peeken, J.C.

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.

Int. J. Radiat. Oncol. Biol. Phys. 125, 1149-1159 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
INTRODUCTION: Radiation-induced pneumonitis (RP) is a side effect after thoracic radiotherapy (RT). The ability to predict RP would facilitate treatment modifications. This study investigates the predictive capacity for symptomatic RP (CTCAE≥2) employing Radiomics and Dosiomics models. METHODS: Computed tomography (CT) scans, along with physical and 2-Gy equivalent dose volumes (EQD2), dose-volume histograms (DVH), and clinical parameters, were evaluated for 708 multicenter lung cancer patients, among whom 89 developed RP≥2. The training cohort consisted of 441 patients from the prospective RTOG 0617 trial. External validation was carried out on 267 patients from the prospective REQUITE study. A Random Forest classifier was employed, with feature selection executed within the inner loop of a 10x5-fold nested cross-validation (nCV) utilizing the minimum-redundancy-maximum-relevance algorithm. To address class imbalances, synthetic oversampling and undersampling were implemented using SMOTE-Tomek. The QUANTEC Normal Tissue Complication Probability (NTCP) model served as a reference. Additionally, the experiments were stratified by subgroups (standard/high-dose and 3D-conformal RT (3D-CRT)/intensity-modulated RT (IMRT)). RESULTS: The best radiomics model identified in the nCV was trained on the standard-dose subgroup achieved a test ROC-AUC of 0.56. The baseline NTCP model showed a predictive performance with a ROC-AUC of 0.56, which was largely dependent on radiation technique (ROC-AUCS: 3D-CRT: 0.75, IMRT: 0.50). The DosiomicsEQD2 model, trained on the full training cohort, attained the second-best performance in the nCV, demonstrating the same technique-dependence (ROC-AUC of 0.75 vs. 0.39). Using a DosiomicsEQD2 ensemble model trained separately on 3D-CRT and IMRT subgroups increased overall performance to a testing ROC-AUC of 0.61, outperforming other modeling strategies for IMRT, while being outperformed by clinical models for 3D-CRT. CONCLUSION: This prospective trial-based study reveals an overall limited predictive capacity of radiomics and dosiomics models and a large influence of radiation technique. IMRT-specific models should be investigated further.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Dosiomics ; Lung Cancer ; Ntcp ; Radiation Pneumonitis ; Radiomics ; Side Effects; Risk-factors; Radiotherapy
ISSN (print) / ISBN 0360-3016
e-ISSN 0360-3016
Zeitschrift International Journal of Radiation Oncology, Biology, Physics
Quellenangaben Band: 125, Heft: 4, Seiten: 1149-1159 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort 101 Park Avenue, 24th Floor, New York, Ny, United States
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
Institute for Machine Learning in Biomed Imaging (IML)
Förderungen European Union
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
German Cancer Consortium (DKTK).