A CT-based novel model to predict pathological complete response of locally advanced esophageal squamous cell carcinoma to neoadjuvant PD-1 blockade in combination with chemotherapy

Purpose To develop a novel CT-based model to predict pathological complete response (pCR) of locally advanced esophageal squamous cell carcinoma (ESCC) to neoadjuvant PD-1 blockade in combination with chemotherapy. Methods 117 consecutive patients with locally advanced ESCC were stratified into training cohort (n=82) and validation cohort (n=35). All patients underwent non-contrast and contrast-enhanced thoracic and upper abdominal CT before neoadjuvant PD-1 blockade in combination with chemotherapy (CTpre), and after two cycles of the therapy before esophagectomy (CTpost), respectively. Univariate analyses and binary logistic regression analyses of ESCC quantitative and qualitative CT features were performed to determine independent predictors of pCR. Prediction performance of the model developed with independent predictors from training cohort was evaluated by receiver operating characteristic (ROC) analysis, and validated by Kappa test in validation cohort. Results In training cohort, the difference in CT attenuation between tumor and background normal esophageal wall obtained from CTpre (ΔTNpre), tumoral increased CT attenuation after contrast-enhanced scan from CTpost images (ΔTpost) and gross tumor volume (GTV) from CTpre were independent predictors of pCR (odds ratio=1.128 (95% confidence interval (CI): 0.997−1.277), 1.113 (95%CI: 0.965−1.239) and 1.133 (95%CI: 1.043−1.231), respectively, all P-values<0.05). Logistic regression model equation (0.121×ΔTNpre+0.107×ΔTpost+0.125×GTV−9.856) to predict pCR showed the best performance with an area under the ROC of 0.876, compared with each independent predictor. The good performance was confirmed by the Kappa test (K-value = 0.796) in validation cohort. Conclusions This novel model can be reliable to predict pCR to neoadjuvant PD-1 blockade in combination with chemotherapy in locally advanced ESCC.