Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics
10.3760/cma.j.cn113030-20220722-00249
- VernacularTitle:基于剂量组学的食管癌放化疗后放射性肺炎的预测模型
- Author:
Xue BAI
1
;
Jing YANG
;
Lei ZHUANG
;
Danhong ZHANG
;
Ying CHEN
;
Xianghui DU
;
Liming SHENG
Author Information
1. 浙江省肿瘤医院放射物理科，中国科学院杭州医学研究所，杭州 310022
- Keywords:
Esophageal neoplasms;
Squamous cell carcinoma;
Radiotherapy;
Dosiomics;
Prediction model
- From:
Chinese Journal of Radiation Oncology
2023;32(7):620-625
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study the risk factors and prediction model of radiation pneumonitis (RP) after radical chemoradiotherapy for locally advanced esophageal cancer based on dosiomics.Methods:Clinical data of 105 patients with esophageal cancer undergoing radical chemoradiotherapy at Zhejiang Cancer Hospital between January 2020 and August 2021 were retrospectively analyzed. RP was scored using the National Cancer Institute's Common Terminology Criteria for Adverse Events version 5.0 (CTCAE 5.0). Clinical factors, traditional dosimetric features and dosiomics features were collected, respectively. The features for predicting PR were analyzed by limma package. Support vector machine, k-nearest neighbor, decision tree, random forest and extreme gradient boosting were used to establish the prediction model, and the ten-fold cross-validation method was employed to evaluate the performance of the model. The differences of this model when different features were chosen were analyzed by delong test.Results:The incidence of RP in the whole group was 21.9%. One clinical factor, 6 traditional dosimetric features and 42 dosiomics features were significantly correlated with the occurrence of RP (all P<0.05). Support vector machine using linear kernel function yielded the optimal prediction performance, and the area under the receiver operating characteristic (ROC) without and with dosiomics features was 0.72 and 0.75, respectively. The models established by support vector machine, random forest and extreme gradient boosting were significantly different with and without dosiomics features (all P<0.05). Conclusion:The addition of dosiomics features can effectively improve the performance of the prediction model of RP after radiotherapy for esophageal cancer.
