Construction of a diagnostic nomogram model for predicting the risk of lymph node metastasis in colon cancer: a retrospective study based on the SEER database

Abstract

Abstract Background. Poor prognosis and frequent recurrence of colon cancer may be associated with lymph node metastasis. Early identification or prediction of lymphatic metastasis in colon cancer is important for improving treatment strategies and patient prognosis. In this study, we aimed to assess the rate of lymph node metastasis in patients with stage pT1 or pT2 colon cancer and screen for independent risk factors to develop a prediction model for the diagnosis of lymph node metastasis. Methods. According to the inclusion and exclusion criteria, 32,803 patients with stage pT1 or pT2 colon cancer who had undergone surgery were selected from the US Surveillance, Epidemiology, and End Results database. The predictive nomogram was internally validated using the validation set. Independent risk factors for lymph node metastasis were identified using univariate and multivariate logistic regression analysis. The discriminatory power, accuracy, and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis, respectively. Results. A nomogram for predicting the risk of lymph node metastasis was developed using six independent risk factors identified through univariate and multivariate analyses. Calibration curve analysis demonstrated good agreement between the nomogram prediction and actual observation. Decision curve analysis showed excellent clinical utility of the prediction model. ROC curve analysis showed that the area under the curve (AUC) of the ROC of the predictive nomogram for lymph node metastasis risk was 0.6714 (95% CI: 0.6621–0.6806) in the training set and 0.6567 (95% CI: 0.6422–0.6712) in the validation set, indicative of good discriminatory power of the model. Conclusion. The novel nomogram established in this study can effectively predict the risk of lymph node metastasis in individual patients with stage pT1 or pT2 colon cancer, which allows clinicians to develop optimal treatment plans.