Dosiomics-based prediction of incidence of radiation pneumonitis in lung cancer patients / 中华放射肿瘤学杂志

ABSTRACT

Objective:To explore the potential of dosiomics in predicting the incidence of radiation pneumonitis by extracting dosiomic features of definitive radiotherapy for lung cancer, and building a machine learning model.Methods:The clinical data, dose files of radiotherapy, planning CT and follow-up CT of 314 patients with lung cancer undergoing definitive radiotherapy were collected retrospectively. According to the clinical data and follow-up CT, the radiation pneumonia was graded, and the dosiomic features of the whole lung were extracted to establish a machine learning model. Dosiomic features associated with radiation pneumonia by LASSO-LR with 1000 bootstrap and AIC backward method with 1000 bootstraps were selected. Training cohort and validation cohort were randomly divided on the basis of 73.Logistic regression was used to establish the prediction model, and ROC curve and calibration curve were adopted to evaluate the performance of the model.Results:A total of 120 dosiomic features were extracted. After LASSO-LR dimensionality reduction, 12 features were selected into the "feature pool".After AIC, 6 dosiomic features were finally selected for model construction. The AUC of training cohort was 0.77(95% CI 0.65 to 0.87), and the AUC of validation cohort was 0.72 (95% CI 0.64 to 0.81). Conclusion:The dosiomics prediction model has the potential to predict the incidence of radiation pneumonia, but it still needs to include multicenter data and prospective data.