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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., DOI: 10.1016/j.ijrobp.2026.01.031 (2026)
Postprint DOI PMC
Open Access Green
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
ISSN (print) / ISBN 0360-3016
e-ISSN 0360-3016
Zeitschrift International Journal of Radiation Oncology, Biology, Physics
Verlag Elsevier
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
Institute for Machine Learning in Biomed Imaging (IML)