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Abstract

Liver metastasis in colorectal cancer (CRC) is common and has an unfavourable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent clinicopathologic predictive factors, and a nomogram was constructed to predict CSS and OS. Multivariate analysis demonstrated age, tumour location, differentiation, gender, TNM stage, chemotherapy, number of sampled lymphnodes, number of positive lymphnodes, tumour size, and metastatic surgery as independent predictors for CRCLM. Nomogram incorporating the ten predictors was constructed. The nomogram showed favourable sensitivity at predicting 1-, 3-, and 5-year OS, with area under receiver operating characteristic curve (AUROC) of 0.816, 0.782, and 0.787 in the training cohort, 0.827, 0.769, and 0.774 in the internal validation cohort, and 0.819, 0.745, and 0.767 in the external validation cohort, respectively. For CSS, the values were 0.825, 0.771, and 0.772 in the training cohort, 0.828, 0.753, and 0.758 in the internal and 0.828, 0.737, and 0.772 in the external validation cohorts, respectively. Calibration curves and ROC curves revealed that using our models to predict the OS and CSS would add more benefit than other single method. In summary, The novel nomogram based on significant clinicopathological characteristics can be conveniently used to facilitate the postoperative individualized prediction of OS and CSS in CRCLM patients.