TY  - JOUR
AU  - Reuter, Lukas M
AU  - Kraus, Kim
AU  - Fischer, Stefan M
AU  - Pletzer, Danai
AU  - Bernhardt, Denise
AU  - Combs, Stephanie E
AU  - Schnabel, Julia A
AU  - Peeken, Jan C
TI  - 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.
JO  - International journal of radiation oncology, biology, physics
VL  - nn
SN  - 0360-3016
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - DKFZ-2026-00417
SP  - nn
PY  - 2026
N1  - epub
AB  - 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.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)).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.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.
KW  - Dosiomics (Other)
KW  - Lung Cancer (Other)
KW  - NTCP (Other)
KW  - Radiation pneumonitis (Other)
KW  - Radiomics (Other)
KW  - Side Effects (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:41720170
DO  - DOI:10.1016/j.ijrobp.2026.01.031
UR  - https://inrepo02.dkfz.de/record/310004
ER  -