Abstract OBJECTIVES Neoadjuvant therapy (NAT) significantly improves the pathologic complete response (pCR) rates in patients with locally advanced oesophageal squamous cell carcinoma (ESCC). Emerging evidence suggests that patients with pCR may experience favourable outcomes and could be considered for active surveillance strategies to delay surgery. This study aims to develop a clinical-radiomics model to predict pCR after NAT in ESCC. METHODS We retrospectively enrolled 236 patients with locally advanced ESCC who received NAT at our center and randomly assigned them to training and test cohorts (3:2 ratio). Radiomics features were extracted from tumour regions segmented on post-NAT contrast-enhanced CT scans. After feature selection, a predictive model integrating radiomics and clinical variables was developed using logistic regression and visualized as a nomogram. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS The clinical-radiomics model achieved an AUC of 0.91 (95% CI: 0.86–0.95), accuracy of 0.84, sensitivity of 0.89, and specificity of 0.81 in the training cohort, and an AUC of 0.84 (95% CI: 0.76–0.92), accuracy of 0.78, sensitivity of 0.84, and specificity of 0.74 in the test cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes, and decision curve analysis confirmed the model’s clinical utility. CONCLUSIONS The clinical-radiomics model accurately predicts pCR following NAT in ESCC and may guide personalized treatment strategies.
Shi et al. (Sun,) studied this question.