Predicting the effectiveness of neoadjuvant therapy in rectal cancer patients: Model construction based on radiomics and carcinoembryonic antigens

This study aimed to develop a multimodal imaging histological model based on computed tomography (CT) images and carcinoembryonic antigen (CEA) values to predict the efficacy of preoperative neoadjuvant therapy in rectal cancer patients. Data were obtained from the Database of Colorectal Cancer of West China Hospital of Sichuan University. A total of 155 patients were enrolled and categorized into good and poor response groups based on pathological evaluation using the tumor regression grade system. Radiomics features were extracted from CT images using PyRadiomics software, and CEA data were collected and processed. Three types of models—a clinical model, a pure radiomics model, and an integrated model—were constructed using logistic regression, support vector machine, random forest (RF), and XGBoost algorithms. The results showed that the integrated model, particularly the RF and XGBoost models, demonstrated the best predictive performance. The RF model achieved an area under the curve (AUC) value of 0.96 in the test set, with accuracy, sensitivity, and specificity of 0.88, 0.50, and 1.00, respectively. The XGBoost model had the highest AUC value of 0.97 in the test set, with accuracy, sensitivity, and specificity of 0.91, 0.70, and 0.97, respectively. This model can be integrated into existing clinical practice to provide clinicians with additional insights for guiding treatment decisions. Future studies should recruit a larger and more diverse patient population to validate and refine the model, and prospective validation is needed to assess its real-world applicability.