CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma

Chao Li; Yuteng Pan; Xianghui Yang; Di Jing; Yu Chen; Chenhua Luo; Jianfeng Qiu; Yongmei Hu; Zijian Zhang; Liting Shi; Liangfang Shen; Rongrong Zhou; Shanfu Lu; Xiang Xiao; Tingyin Chen

doi:10.3389/fonc.2023.1219106

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma

Front Oncol. 2023 Aug 23:13:1219106. doi: 10.3389/fonc.2023.1219106. eCollection 2023.

Authors

Chao Li^{1

2}, Yuteng Pan³, Xianghui Yang⁴, Di Jing¹, Yu Chen¹, Chenhua Luo⁵, Jianfeng Qiu⁶, Yongmei Hu¹, Zijian Zhang¹, Liting Shi⁶, Liangfang Shen¹, Rongrong Zhou¹, Shanfu Lu⁷, Xiang Xiao⁸, Tingyin Chen⁹

Affiliations

¹ Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
² Department of Radiation Oncology, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China.
³ Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
⁴ Department of Oncology, Changsha Central Hospital, Changsha, Hunan, China.
⁵ Xiangya School of Medicine, Central South University, Hunan, Changsha, China.
⁶ Medical Engineering and Technology Research Center, Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China Technology, Shenzhen, Guangdong, China.
⁷ Perception Vision Medical Technologies Co. Ltd, Guangzhou, China.
⁸ Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, Changsha, China.
⁹ Department of Network and Information Center, Xiangya Hospital, Central South University, Hunan, Changsha, China.

Abstract

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images.

Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS.

Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively.

Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Keywords: esophageal carcinoma; planning ct; radiation oncology; radiomics; treatment response.

Grants and funding

This work was funded by Academic Promotion Program of Shandong First Medical University (grant number: 2019QL009), the Taishan Scholars Program of Shandong Province (grant number: TS201712065) and Mobile Health Ministry of Education, China Mobile Joint Laboratory Project (grant number: 2020MHL02015).