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Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics
Bai Xue1,2,3, Yang Jing1,2, Zhuang Lei4, Zhang Danhong2,5, Chen Ying2,5, Du Xianghui2,5, Sheng Liming2,5
1Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China; 2Zhejiang Key Laboratory of Thoracic Oncology, Hangzhou 310022, China; 3Zhejiang Key Laboratory of Radiation Oncology, Hangzhou 310022, China; 4the Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China; 5Department of Thoracic Radiotherapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
AbstractObjective 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.
Fund:National Natural Science Foundation of China (12005190); Beijing Bethune Charitable Foundation (flzh202114); Open Project of Zhejiang Key Laboratory of Radiation Oncology (2022ZJCCRAD08)
Bai Xue,Yang Jing,Zhuang Lei et al. Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics[J]. Chinese Journal of Radiation Oncology, 2023, 32(7): 620-625.
Bai Xue,Yang Jing,Zhuang Lei et al. Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics[J]. Chinese Journal of Radiation Oncology, 2023, 32(7): 620-625.
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2023, Vol. 32 
