Xu Yuxin, Wang Xiaojie, Huang Ying, Ye Daoxiong, Chi Pan
Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China.
Transl Cancer Res. 2022 Aug;11(8):2795-2809. doi: 10.21037/tcr-20-1860.
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.
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.
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.
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.
通过基于最小绝对收缩和选择算子（LASSO）的Cox回归，开发并验证了用于预测同步性结直肠癌（SCC）患者术后总生存期（OS）的列线图。
数据来自2004年至2013年间被诊断为结直肠癌（CRC）超过一次的患者的SEER数据库。排除患有超过3次CRC或多个异时性原发性癌的患者。通过K自适应划分算法和x-tile软件确定连续变量的截断点。使用基于LASSO的Cox回归构建预测SCC患者OS的模型，进行内部和外部验证，并通过校准曲线、C指数、赤池信息准则（AIC）、贝叶斯信息准则（BIC）、净重新分类改善（NRI）、综合判别改善（IDI）、时间依赖性受试者工作特征曲线（timeROC）、时间依赖性曲线下面积（timeAUC）和决策曲线分析（DCA）进行评估，并将结果与Cox回归开发的模型进行比较。
发现SCC患者年龄较大，男性居多，且T3浸润深度的可能性较大。此外，基于LASSO的Cox回归开发的模型与Cox回归在C指数（0.712和0.710）、AIC（33420和33431）、BIC（4.49）、IDI（0.002）、NRI（-0.009）、timeROC和DCA方面无显著差异。此外，基于LASSO的Cox回归开发的模型在timeAUC方面表现优于Cox回归。此外，基于LASSO的Cox回归开发的模型显示出良好的C指数（0.712、0.637和0.651）、AIC（33420、34043和33994）、BIC（1178.76和1098.57）、IDI（-0.072和-0.064）、NRI（0.525和0.466）、timeROC、timeAUC，并且与首次TNM分期和两次TNM分期的组合相比，具有更大的净效益。
本研究表明，应对年龄较大、男性和T3患者进行密切随访。与传统的Cox回归模型相比，基于LASSO的Cox回归减少了模型变量，避免了过度拟合和共线性，具有临床意义。
