A New Survival Prediction Model for Patients with Synchronous Colorectal Carcinomas Based on SEER

Introduction: The nomogram for postoperative prediction of overall survival (OS) in patients’ synchronous colorectal carcinomas (SCC) was developed and validated by LASSO regression combined with COX regression. Methods The data was obtained from the SEER database of patients diagnosed with colorectal cancer (CRC) more than one time between 2004 and 2013. The cut-off points for the continuous variable were identified by the K-adaptive partitioning algorithm and x-tile software. Using LASSO regression combined with the Cox regression, a model for predicting the overall survival of SCC was built, internally and externally validated, and measured through a calibration curve, C-index, AIC, BIC, IDI, NRI, timeROC, timeAUC, and decision curve analysis (DCA), and results compared to the model developed by the Cox regression. Results Patients with SCC were found to be older, more often men, and likely to have a depth of invasion by T3. In addition, there were no significant differences between the model developed by LASSO regression combined with Cox regression and the Cox regression in the calibration curve, C-index, AIC, BIC, IDI, NRI, timeROC, and DCA. Besides, the model developed by LASSO regression combined with Cox regression was found to perform better than the Cox regression in the timeAUC. Moreover, the model developed by LASSO regression combined with Cox regression showed good calibration, C-index, AIC, BIC, IDI, NRI, timeROC, timeAUC and had a larger net benefit compared to both the first time TNM staging and the combination of two times TNM staging. Discussion This present study indicates that a close follow-up of older patients, male, and T3 should be made. LASSO regression combined with COX regression decreases the variables of the model, avoids overfitting and collinearity and has clinical significance.