Purpose: To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. Methods: 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. Results: There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). Conclusion: We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.

Radiation esophagitis (RE) is an adverse reaction to radiotherapy for thoracic malignancies and a major factor limiting the dosage for thoracic tumors. In cases of RE, mild conditions may involve only discomfort in the throat, severe cases can present with intense pain and significant difficulty swallowing, and may lead to interruptions in radiation therapy, affecting the treatment outcome. However, much of the current research focuses on how to diagnose and treat the disease and needs to address the prevention of RE. In this review, several online databases, including PubMed, Web of Science, Google Scholar, the China National Knowledge Infrastructure, and the Chinese Medical Journal Database, were searched to collect relevant published studies. This review provides updated information on the pathogenesis, diagnosis, risk factors, development of predictive models, prevention, and treatment of RE, aiming to serve as a reference for its diagnosis, treatment, and prevention.