Dosiomics-based prediction of incidence of radiation pneumonitis in lung cancer patients
10.3760/cma.j.cn113030-20211115-00466
- VernacularTitle:基于剂量组学预测肺癌患者放射性肺炎发生的研究
- Author:
Meng YAN
1
;
Zhen ZHANG
;
Jiaqi YU
;
Wei WANG
;
Qingxin WANG
;
Lujun ZHAO
Author Information
1. 天津医科大学肿瘤医院放射治疗科/国家恶性肿瘤临床医学研究中心/天津市肿瘤防治重点实验室/天津市恶性肿瘤临床医学研究中心，天津 300060
- Keywords:
Lung neoplasms;
Dosiomics;
Radiation pneumonitis;
Machine learning
- From:
Chinese Journal of Radiation Oncology
2022;31(8):698-703
- CountryChina
- Language:Chinese
-
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 7:3.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.
