Preliminary study of predicting radiation pneumonitis based on radiomics technology
Zhang Zhen1, Zhao Lujun1, Wang Wei1, Cui Jingjing2, Wang Qi1, Liu Ying3, Wang Qingxin1, Zhang Daguang1
1Department of Radiation Oncology,Tianjin Medical University Cancer Institute and Hospital,National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer,Tianjin 300060, China; 2Department of Innovation Business, Huiying Medical Technology (Beijing) Co., Ltd. Beijing 100192, China; 3Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
Abstract：Objective To identify the radiomics features related to the occurrence of radiation pneumonitis based on localized CT images of the chest in lung cancer patients, establish a machine learning model and investigate the value of radiomics technology in predicting the incidence of radiation pneumonitis. Methods Clinical data of 86 patients with stage Ⅲ non-small cell lung cancer who received radical intensity-modulated radiation therapy (IMRT) were retrospectively analyzed. The radiation pneumonitis was graded by follow-up imaging data and clinical information. The planning CT images were collected. The lung was used as the volume of interest for extraction of radiomics features. The radiomics features,clinical and dosimetric parameters associated with the incidence of radiation pneumonitis were analyzed. Using the support vector machine to construct the model,the prediction performance of the model was evaluated by the five-fold verification method. Results A total of 1029 radiomics features were extracted from CT images and 5 features were selected by ANOVA and LASSO. Two validation sets showed differences between adopting radiomics features alone and incorporating clinical and dosimetric parameters and radiomics features (AUC=0.67 and 0.71,respectively). Conclusions The radiomics model constructed by planning CT images of lung cancer patients has the potential to predict the occurrence of radiation pneumonitis. Addition of clinical and dosimetric parameters can further improve the prediction performance of the model.
Zhang Zhen,Zhao Lujun,Wang Wei et al. Preliminary study of predicting radiation pneumonitis based on radiomics technology[J]. Chinese Journal of Radiation Oncology, 2020, 29(6): 427-431.
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