Study of application of radiomics model in predicting radiation pneumontis in patients with lung cancer and esophageal cancer
Yu Jiaqi1, Zhang Zhen1,2, Ren Kai1, Wang Wei1, Liu Ying3, Li Qian3, Ye Zhaoxiang3, Zhao Lujun1
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 Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University, Maastricht 6229 ET, Netherlands; 3Department of Radiology, 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
Abstract：Objective To analyze and explore the common radiomics features of radiation pneumonitis (RP) in patients with lung cancer and esophageal cancer, and then establish a prediction model that can predict the occurrence of RP in two types of cancer after radiotherapy. Methods Clinical data of 100 patients with stage Ⅲ lung cancer and 100 patients with stage Ⅲ esophageal cancer who received radical radiotherapy were retrospectively analyzed. The RP was graded by imaging data and clinical information during follow-up, and the planning CT images were collected. The whole lung was used as the volume of interest to extract radiomics features. The radiomics features,clinical and dosimetric parameters related to RP were analyzed, and the model was constructed by machine learning. Results A total of 1691 radiomics features were extracted from CT images. After ANOVA and LASSO dimensionality reduction in lung cancer and esophageal cancer patients, 8 and 6 radiomics features associated with RP were identified, and 5 of them were the same. Using the random forest to construct the prediction model,lung cancer and esophageal cancer were alternately used as the training and validation sets. The AUC values of esophageal cancer and lung cancer as the independent validation set were 0.662 and 0.645. Conclusions It is feasible to construct a common prediction model of RP in patients with lung cancer and esophageal cancer. Nevertheless, it is necessary to further expand the sample size and include clinical and dosimetric parameters to increase its accuracy, stability and generalization ability.
Yu Jiaqi,Zhang Zhen,Ren Kai et al. Study of application of radiomics model in predicting radiation pneumontis in patients with lung cancer and esophageal cancer[J]. Chinese Journal of Radiation Oncology, 2021, 30(11): 1111-1116.
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