A Nomogram Model Developed and Validated for The Evaluation of Lymph Node Metastasis in Patients with Rectal Cancer

Purpose: The aim of this study was to develop and validate a nomogram model to evaluate lymph node metastasis (LNM) in patients with rectal cancer (RC). Methods: A total of 162 patients with RC between 2019 and 2021 were included in the study. Patients were allocated to a training set and a validation set at a ratio of 7:3. The lymph node (LN) status was evaluated retrospectively from magnetic resonance imaging (MRI) images by two radiologists. Based on 103 radiomic features extracted from T2 weighted images (T2WI), the least absolute shrinkage and selection operator (LASSO) was used to screen and calculate the radiomic feature score (Radscore). The model was constructed using the logistics regression algorithm. The DeLong test and decision curve analysis (DCA) were used to compare the prediction performance and clinical utility of the MRI reported model, the Radscore model, and the complex model constructed by combining the MRI reported and Radscore. The nomogram model was constructed to visualize the prediction results of the best model. Model performance was evaluated in the training and validation groups, and the calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration. Result: This study included 162 patients with RC, including 54 patients with LNM and 108 patients without LNM. All three models constructed by the logistics regression algorithm were good at identifying LNM. The DeLong test and the DCA results showed that the complex model outperformed the MRI-based model and the Radscore model in relation to their predictive performance and clinical utility. The nomogram of the complex model had an area under the curve (AUC) of 0.902 (95% confidence interval (CI): 0.848−0.957) in the training group and an AUC of 0.891 (95% CI: 0.799−0.983) in the validation group. Meanwhile, the calibration curve and the Hosmer-Lemeshow goodness-of-fit test showed good calibration. Conclusion: The nomogram model constructed based on T2WI radiomics and MRI reported had good diagnostic efficacies for LNM in patients with RC, and provided a new auxiliary method for accurate and individualized clinical management.