A LASSO-based survival prediction model for patients with synchronous colorectal carcinomas based on SEER

BACKGROUND: The nomogram for postoperative prediction of overall survival (OS) in patients’ synchronous colorectal carcinomas (SCC) was developed and validated by least absolute shrinkage and selection operator (LASSO)-based 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. Patients who had CRC more than 3 times or multiple metachronous primary carcinomas were excluded. The cut-off points for the continuous variable were identified by the K-adaptive partitioning algorithm and x-tile software. Using LASSO-based Cox regression, a model for predicting the OS of SCC was built, internally and externally validated, and measured through a calibration curve, C-index, Akaike information criterion (AIC), Bayesian information criterion (BIC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), time-dependent receiver operating characteristic (timeROC), time-dependent area under curve (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-based Cox regression and the Cox regression in the C-index (0.712 and 0.710), AIC (33,420 and 33,431), BIC (4.49), IDI (0.002), NRI (–0.009), timeROC, and DCA. Besides, the model developed by LASSO-based Cox regression was found to perform better than the Cox regression in the timeAUC. Moreover, the model developed by LASSO-based Cox regression showed good C-index (0.712, 0.637, and 0.651), AIC (33,420, 34,043, and 33,994), BIC (1,178.76 and 1,098.57), IDI (–0.072 and –0.064), NRI (0.525 and 0.466), timeROC, timeAUC and had a larger net benefit compared to both the first time TNM staging and the combination of two times TNM staging. CONCLUSIONS: This present study indicates that a close follow-up of older patients, male, and T3 should be made. Compared with the traditional Cox regression model, LASSO-based Cox regression decreases the variables of the model, avoids overfitting and collinearity and has clinical significance.