A Nomogram Model Based on Enhanced CT Radiomics Features for Predicting Tumor Response to Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma
Authors: Kong Jie , Chen Liang , Zhu Shuchai , Shi Gaofeng , Zhang Jun , Liu Zhikun , Ren Jialiang 
Published: November 2021

Objective: Duing to chemoradiotherapy was the standard treatment for moderate and advanced esophageal cancer，tumor response to chemoradiotherapy was the most important prognostic factor.The aim of this study was to determine the value of radiomics in predicting tumor response to chemoradiotherapy in patients with esophageal squamous cell carcinoma.Methods: 218 patients with esophageal squamous cell carcinoma diagnosed by pathology and receiving chemoradiotherapy from July 2016 to December 2017 were collected according to the ratio of 7:3,Patients were randomly divided into the training group (n=153) and the validation group (n=65).Clinical information of patients was collected, enhanced CT images were imported into 3D Slicer software (4.8.1 version), the radiomic features were extracted by the Python programme package.In the training group, LASSO logistic regression was performed to choose the key features and construct a radiological label, the model of predicting tumor response to chemoradiotherapy; for a better prediction, A radiomics nomogram incorporating radiological label was developed on the basis of multivariable logistic regression analysis. Model performance was determined and validated with respect to its discrimination, calibration and prediction. Clinical usefulness was estimated by decision curve analysis. Results: According to the criteria for evaluating the curative effect of radiotherapy by esophagography combined with CT, 62 patients achieved CR, 150 patients achieved PR and 6 cases of NR, and the CR rate was 28.4%, 27.5% in the training group and 30.8% in the validation group (χ2=0.307, p = 0.858).The radiomics signature including seven features was significantly associated with CR after chemoradiotherapy, which constructed the radiological label.The model predicted CR after chemoradiotherapy with an area under curve (AUC) of 0.812 in the training group, and performance was similar in the validation group (AUC 0.835 ). The sensitivity, specificity and accuracy in predicting CR after chemoradiotherapy were 85.7%, 66.4% and 73.2%, respectively. The Kappa coefficient was 0.424;In the validation group,the sensitivity, specificity and accuracy of were 85.0%,62.2% and 69.2%, respectively, and the Kappa coefficient was 0.395.The radiomics nomogram, which included the radiological label and clinical risk factors, predicted CR after chemoradiotherapy with AUC of 0.840 in the training group, and AUC 0.853 in the validation group.The sensitivity, specificity and accuracy of predicting CR were 90.5%, 61.3%, 79.3%, and Kappa coefficient was 0.621.In the validation group, the sensitivity, specificity and accuracy of predicting CR were 90.0%, 54.3% and 65.6%, and the Kappa coefficient was 0.643.Decision curve analysis supported that the radiomics nomogram was superior to radiological label.Conclusion: The radiological label based on enhanced CT was a potentially tool for predicting CR after chemoradiotherapy. The radiomics nomogram model had a better prediction ability than other models and could be used as a quantitative model to predict CR after chemoradiotherapy.