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IntroductionThis study aimed to explore independent risk and prognostic factors in elderly patients with colorectal cancer liver metastasis (ECRLM) and generate nomograms for predicting the occurrence and overall survival (OS) rates of such patients.MethodElderly colorectal cancer patients (ECRC) from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. External validation relied on Chinese patients from the China-Japan Union Hospital of Jilin University. Univariate and multivariate logistic regression analyses were employed to identify liver metastasis (LM) risk variables, which were used to create a nomogram to estimate LM probabilities in patients with ECRC. Univariate and multivariable Cox analyses were performed to identify prognostic variables and further derive nomograms that could predict the OS of patients with ERCLM. Differences in lifespan were assessed using the Kaplan–Meier analysis. Finally, the quality of the nomograms was verified using decision curve analysis (DCA), calibration curves, and receiver operating characteristic curves (ROC).ResultIn the SEER cohort, 32,330 patients were selected, of those, 3,012 (9.32%) were diagnosed with LM. A total of 188 ECRLM cases from a Chinese medical center were assigned for external validation. LM occurrence can be affected by 13 factors, including age at diagnosis, marital status, race, bone metastases, lung metastases, CEA level, tumor size, Grade, histology, primary site, T stage, N stage and sex. Furthermore, in ECRLM patients, 10 variables, including age at diagnosis, CEA level, tumor size, lung metastasis, bone metastasis, chemotherapy, surgery, N stage, grade, and race, have been shown to be independent prognostic predictors. The results from both internal and external validation revealed a high level of accuracy in predicting outcomes, as well as significant clinical utility, for the two nomograms.ConclusionWe created two nomograms to predict the occurrence and prognosis of LM in patients with ECRC, which would contribute significantly to the improvement in disease detection accuracy and the formulation of personalized cures for that particular demographic. |
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