Clinical characteristics and prognosis analysis of postoperative patients with stage I–III colon cancer based on SEER database

Purpose To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model. Methods Data on postoperative stage I–III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group. Results Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects. Conclusion Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients (AU)

Clinical characteristics and prognosis analysis of postoperative patients with stage I-III colon cancer based on SEER database.

PURPOSE: To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model. METHODS: Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group. RESULTS: Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects. CONCLUSION: Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.