Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease

Background Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But predictive models integrating multimodal data are lacking. This study aimed to develop a novel machine learning-based nomogram integrating clinical, dosimetric, and inflammatory predictors for RP risk assessment in this high-risk population.Methods This retrospective study of 424 ILD patients collected clinical, dosimetric, and inflammatory data. Machine learning algorithms created composite dosimetric (D score) and inflammatory (Inflamm score) scores. A multivariable logistic regression nomogram was built incorporating these scores with clinical risk factors. Model performance was assessed using area under the curve (AUC), calibration curve, and decision curve analysis (DCA).Results RP occurred in 200 (47%) patients. Independent risk factors included higher performance status, Charlson comorbidity index (CCI), usual interstitial pneumonia (UIP) pattern, immunotherapy, concurrent chemoradiotherapy, more radiation sessions, and lower lung volume. The D score and Inflamm score were both independent predictors. The integrated nomogram (AUC = 0.929) showed excellent discrimination, significantly outperforming the clinical model (AUC = 0.86), D score (AUC = 0.758) (both p < 0.001), and Inflamm score (AUC = 0.910, p = 0.168). Calibration curve and DCA confirmed its strong calibration ability and clinical utility to identify high-risk patients early.Conclusion The integrated nomogram combining clinical, dosimetric, and inflammatory predictors enables accurate, individualized RP risk assessment in lung cancer patients with ILD. It can guide adjustments to individualized radiotherapy plans or preventive interventions, supporting better patient selection, treatment decisions, and proactive follow-up.