Abstract
PURPOSE: This study aimed to investigate the ability of enhanced computed tomography (CT)-based radiomics and dosimetric parameters in predicting response to radiotherapy for esophageal cancer. METHODS: A retrospective analysis of 147 patients diagnosed with esophageal cancer was performed, and the patients were divided into a training group (104 patients) and a validation group (43 patients). In total, 851 radiomics features were extracted from the primary lesions for analysis. Maximum correlation minimum redundancy and minimum least absolute shrinkage and selection operator were utilized for feature screening of radiomics features, and logistic regression was applied to construct a radiotherapy radiomics model for esophageal cancer. Finally, univariate and multivariate parameters were used to identify significant clinical and dosimetric characteristics for constructing combination models. The area evaluated the predictive performance under the receiver operating characteristics (AUC) curve and the accuracy, sensitivity, and specificity of the training and validation cohorts. RESULTS: Univariate logistic regression analysis revealed statistically significant differences in clinical parameters of sex (p=0.031) and esophageal cancer thickness (p=0.028) on treatment response, whereas dosimetric parameters did not differ significantly in response to treatment. The combined model demonstrated improved discrimination between the training and validation groups, with AUCs of 0.78 (95% confidence interval [CI], 0.69-0.87) and 0.79 (95% CI, 0.65-0.93) in the training and validation groups, respectively. CONCLUSION: The combined model has potential application value in predicting the treatment response of patients with esophageal cancer after radiotherapy.