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

Key Points

• The study aims to develop a machine learning model to predict radiation pneumonitis (RP) risk in lung cancer patients with interstitial lung disease (ILD).
• Developed an integrated nomogram using clinical, dosimetric, and inflammatory predictors.
• Utilized patient data to create individualized risk profiles.
• Evaluated the accuracy of the model in predicting RP risk.
• The integrated nomogram provides accurate and individualized RP risk assessments.
• It supports better patient selection and treatment decisions.
• Facilitates proactive follow-up and preventive interventions for at-risk patients.