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In International journal of radiation oncology, biology, physics

PURPOSE : To develop and validate a pretreatment CT-based deep-learning (DL) model for predicting the treatment response to concurrent chemoradiotherapy (CCRT) among patients with locally advanced thoracic esophageal squamous cell carcinoma (TESCC).

MATERIALS AND METHODS : We conducted a prospective, multicenter study on the therapeutic efficacy of CCRT among TESCC patients across 9 hospitals in China (ChiCTR**********). A total of 306 patients with locally advanced TESCC diagnosed by histopathology from August 2015 to May 2020 were included in this study. A 3D deep-learning radiomics model (3D-DLRM) was developed and validated based on pretreatment CT images to predict the response to CCRT. Furthermore, the prediction performance of the newly developed 3D-DLRM was analyzed according to 3 categories: radiotherapy plan, radiation field, and prescription dose used.

RESULTS : The 3D-DLRM achieved good prediction performance, with areas under the receiver operating characteristic curve (AUCs) of 0.897 (95% CI 0.840-0.959) for the training cohort and 0.833 (95% CI 0.654-1.000) for the validation cohort. Specifically, the 3D-DLRM accurately predicted patients who would not respond to CCRT, with a positive predictive value (PPV) of 100% for the validation cohort. Moreover, the 3D-DLRM performed well in all 3 categories, each with AUCs > 0.8 and PPVs of approximately 100%.

CONCLUSION : The proposed pretreatment CT-based 3D-DLRM provides a potential tool for predicting the response to CCRT among patients with locally advanced TESCC. With the help of precise pretreatment prediction, we may guide the individualized treatment of patients and improve survival.

Li Xiaoqin, Gao Han, Zhu Jian, Huang Yong, Zhu Yongbei, Huang Wei, Li Zhenjiang, Sun Kai, Liu Zhenyu, Tian Jie, Li Baosheng