AUTHOR=Ma Zhenyu , Yang Shuping , Yang Yalin , Luo Jingran , Zhou Yixiao , Yang Huiyong 

TITLE=Development and validation of prediction models for the prognosis of colon cancer with lung metastases: a population-based cohort study

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1073360

DOI=10.3389/fendo.2023.1073360

ISSN=1664-2392

ABSTRACT=Background
Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients.
Method 
The data on 1284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with (7:3, stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the LASSO and multivariate Cox regression, the suitable predictors were entered into Cox-proportional-hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups. 
Results
Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in high-risk group than in low-risk group, which suggested that the models had significant discrimination for patients with different prognosis. 
Conclusion
After verification, our prediction models of CCLM are reliable, and can predict the OS and CSS of CCLM patients in the next 1-, 3-, and 5-year, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.