A nomogram model based on cone beam CT radiomics combined with clinical features and dosimetric parameters predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy
Du Feng1,2, Wang Qiang1, Wang Wei3, Zhang Yingjie3, Li Zhenxiang3, Li Jianbin3
1Department of Radiation Oncology, Zibo Municipal Hospital, Zibo 255400, China; 2School of Clinical Medicine, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China; 3Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji'nan 250117, China
Abstract：Objective To develop and validate a nomogram model for predicting radiation-induced pneumonitis in esophageal cancer based on CBCT radiomics characteristics combined with clinical characteristics and lung dosimetric parameters. Methods Clinical data, dosimetric parameters and CBCT images of 96 patients with thoracic middle esophageal squamous cell carcinoma treated by intensity-modulated radiation therapy (IMRT) from 2017 to 2019 were analyzed retrospectively. The CBCT images of each patient in three different time periods were obtained. All patients were assigned randomly into the primary cohort (n=67) and validation cohort (n=29). Double lungs were selected as the region of interest (ROI), and 3D-slicer software was used for image segmentation and feature extraction. The LASSO regression were applied to identify candidate radiomic features and construct the Rad-score. The optimal time period, clinical and dosimetric parameters were selected to construct the nomogram model, and then the area under the receiver operating characteristic curve (AUC) was used to evaluate the prediction effect of the model. Results The predictive capacity of the model in the first time period was the highest. In the primary cohort,the AUC was 0.700(95%CI:0.568-0.832), the sensitivity was 61.5%, and the specificity was 75.0%. In the validation cohort, the AUC was 0.765(95%CI:0.588-0.941),the sensitivity was 84.6% and the specificity was 64.7%,respectively. In the combined nomogram model,the AUC in the primary cohort was 0.836(95%CI:0.700-0.918), the sensitivity was 96.0% and the specificity was 54.8%. In the validation cohort,the AUC was 0.905(95%CI:0.799-1.000), the sensitivity was 92.9% and the specificity was 73.3%,respectively. The diagnostic efficiency of combined nomogram model was the best. Conclusions The nomogram model based on early lung CBCT radiomics has certain predictive efficiency for RP. The model of lung CBCT radiomics in early stage of radiotherapy can predict RP of esophageal cancer. The nomogram model based on Rad-score combined with V5Gy, MLD and tumor stage yields better predictive accuracy, which can be used as a quantitative prediction model for RP.
Du Feng,Wang Qiang,Wang Wei et al. A nomogram model based on cone beam CT radiomics combined with clinical features and dosimetric parameters predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy[J]. Chinese Journal of Radiation Oncology, 2021, 30(6): 549-555.
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