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.
[PURPOSE] Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT).
APA
Reuter LM, Kraus KM, et al. (2026). 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.. International journal of radiation oncology, biology, physics. https://doi.org/10.1016/j.ijrobp.2026.01.031
MLA
Reuter LM, et al.. "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.." International journal of radiation oncology, biology, physics, 2026.
PMID
41720170
Abstract
[PURPOSE] Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatment modifications. This study investigates the predictive capacity for symptomatic RP (Common Terminology Criteria for Adverse Events ≥ 2) employing Radiomics and Dosiomics models.
[METHODS AND MATERIALS] Computed tomography scans, along with physical and 2-Gy equivalent dose volumes (EQD2), dose-volume histograms, 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 (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) 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 model served as a reference. Additionally, the experiments were stratified by subgroups (standard/high-dose and 3-dimensional 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 Normal Tissue Complication Probability 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 Dosiomics EQD2 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 Dosiomics EQD2 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.
[CONCLUSIONS] 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.
[METHODS AND MATERIALS] Computed tomography scans, along with physical and 2-Gy equivalent dose volumes (EQD2), dose-volume histograms, 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 (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) 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 model served as a reference. Additionally, the experiments were stratified by subgroups (standard/high-dose and 3-dimensional 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 Normal Tissue Complication Probability 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 Dosiomics EQD2 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 Dosiomics EQD2 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.
[CONCLUSIONS] 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.