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...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： t 14; validation cohort： n：40） withpathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. Highthroughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomicssignature was constructed using the least absolute shrinkage and selection operator （Lasso）. Associations betweenradiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomicsapproach and tumor volume for discriminating between stages Ⅰ-Ⅱand Ⅲ-Ⅳ was evaluated and compared usingthe 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 radiomicssignature after feature dimension reduction. The radiomics signature was significantly associated with ESCCstaging （P〈0.001）, and yielded a better performance for discrimination of early and advanced stage ESCC comparedto tumor volume in both the primary [area under the receiver operating characteristic curve （AUC）： 0.795 vs. 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-Ⅱand Ⅲ-Ⅳ ESCC beforetreatment.展开更多
基金supported by the National Key R&D Program of China (No. 2017YFC1309100)National Natural Scientific Foundation of China (No. 81771912)Science and Technology Planning Project of Guangdong Province (No. 2017B020227012)
文摘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： t 14; validation cohort： n：40） withpathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. Highthroughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomicssignature was constructed using the least absolute shrinkage and selection operator （Lasso）. Associations betweenradiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomicsapproach and tumor volume for discriminating between stages Ⅰ-Ⅱand Ⅲ-Ⅳ was evaluated and compared usingthe 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 radiomicssignature after feature dimension reduction. The radiomics signature was significantly associated with ESCCstaging （P〈0.001）, and yielded a better performance for discrimination of early and advanced stage ESCC comparedto tumor volume in both the primary [area under the receiver operating characteristic curve （AUC）： 0.795 vs. 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-Ⅱand Ⅲ-Ⅳ ESCC beforetreatment.
