End-to-End Pretreatment Prediction of Radiation Pneumonitis in Patients With Non-Small Cell Lung Cancer Using...

Radiation pneumonitis (RP) is the most common toxicity after thoracic radiotherapy. We develop an artificial intelligence model to predict RP in an institutional cohort of patients undergoing radiotherapy for non-small cell lung cancer.

Data were collected from patients diagnosed between 2002 and 2020. Patients were screened for a known survival/RP outcome, as well as treatment and clinical parameters. A transformer, pretrained on an open-source data set, was first trained to predict abnormal versus normal pulmonary function based on computed tomography (CT) scans. Transfer learning was then used to apply this model to the RP data set. Three clinical-plus-dosimetric variable models were trained. Finally, a model that combined the CT-based risk score and clinical/dosimetric variables was also trained, to explore if the CT-based risk score improved risk stratification. All models were cross-validated.

1,023 patients were included in the RP data set, for a total of 2,257 pretreatment scans, with a 15% RP rate. The clinical-plus-dosimetric-only values were 0.70, 0.70, and 0.71, and the CT-only was 0.66. Combining the CT-based risk score and clinical parameters improved the receiver operating characteristic curve to a value of 0.74, averaged across all folds. The combined model also had superior sensitivity for a fixed specificity value of 60%. Precision-recall metrics were comparable across models. Activation mapping of the CT-only model showed prioritization of upper lung and right lung.

In a cohort treated heterogeneous radiotherapy techniques and doses, combining CT-based risk scores with clinical values enhances the prediction of RP. This suggests that CT scans contain additional information that has the potential to enhance RP predictions. Activation score mapping shows focus on lung structure, upper lung, and right lung. Model code is available online.