Abstract
PURPOSE: We aimed to integrate MR radiomics and dynamic hematological factors to build a model to predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in esophageal squamous cell carcinoma (ESCC). METHODS: Patients with ESCC receiving NCRT and esophagectomy between September 2014 and September 2022 were retrospectively included. All patients underwent pre-treatment T2-weighted imaging as well as pre-treatment and post-treatment blood tests. Patients were randomly divided to training set and testing set at a ratio of 7:3. Machine learning models were constructed based on MR radiomics and hematological factors to predict pCR, respectively. A nomogram model was developed to integrate MR radiomics and hematological factors. Model performances were evaluated by areas under curves (AUCs), sensitivity, specificity, positive predictive value and negative. RESULTS: A total of 82 patients were included, of whom 39 (47.6 %) achieved pCR. The hematological model built with four hematological factors had an AUC of 0.628 (95%CI 0.391-0.852) in the testing set. Two out of 1106 extracted features were selected to build the radiomics model with an AUC of 0.821 (95%CI 0.641-0.981). The nomogram model integrating hematological factors and MR radiomics had best predictive performance, with an AUC of 0.904 (95%CI 0.770-1.000) in the testing set. CONCLUSION: An integrated model using dynamic hematological factors and MR radiomics is constructed to accurately predicted pCR to NCRT in ESCC, which may be potentially useful to assist individualized preservation treatment of the esophagus.