Lei Wu1,2, Cong Wang3, Xianzheng Tan2, Zixuan Cheng1,2, Ke Zhao1,2, Lifen Yan2, Yanli Liang2, Zaiyi Liu2, Changhong Liang1,2. 1. School of Medicine, South China University of Technology, Guangzhou 510006, China. 2. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. 3. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Abstract
OBJECTIVE: To predict preoperative staging using a radiomics approach based on computed tomography (CT) images of patients with esophageal squamous cell carcinoma (ESCC). METHODS: This retrospective study included 154 patients (primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator (Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages I-II and III-IV was evaluated and compared using the receiver operating characteristics (ROC) curves and net reclassification improvement (NRI). RESULTS: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging (P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve (AUC): 0.795vs. 0.694, P=0.003; NRI=0.424)] and validation cohorts (AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834). CONCLUSIONS: The quantitative approach has the potential to identify stage I-II and III-IV ESCC before treatment.
OBJECTIVE: To predict preoperative staging using a radiomics approach based on computed tomography (CT) images of patients with esophageal squamous cell carcinoma (ESCC). METHODS: This retrospective study included 154 patients (primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator (Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages I-II and III-IV was evaluated and compared using the receiver operating characteristics (ROC) curves and net reclassification improvement (NRI). RESULTS: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging (P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve (AUC): 0.795vs. 0.694, P=0.003; NRI=0.424)] and validation cohorts (AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834). CONCLUSIONS: The quantitative approach has the potential to identify stage I-II and III-IV ESCC before treatment.
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