Risk factors and predictors of lymph nodes metastasis and distant metastasis in newly diagnosed T1 colorectal cancer

Colorectal Cancer

Guo K, et al. Cancer Med 2020.


BACKGROUND: Lymph nodes metastasis (LNM) and distant metastasis (DM) are important prognostic factors in colorectal cancer (CRC) and determine the following treatment approaches. We aimed to find clinicopathological factors associated with LNM and DM, and analyze the prognosis of CRC patients with T1 stage.

METHODS: A total of 17 516 eligible patients with T1 CRC were retrospectively enrolled in the study based on the Surveillance, Epidemiology, and End Results (SEER) database during 2004-2016. Logistic regression analysis was performed to identify risk factors for LNM and DM. Unadjusted and adjusted Cox proportional hazard models were used to identify prognostic factors for overall survival. We performed the cumulative incidence function (CIF) to further determine the prognostic role of LNM and DM in colorectal cancer-specific death (CCSD). LNM, DM, and OS nomogram were constructed based on these models and evaluated by the C-index and calibration plots for discrimination and accuracy, respectively. The clinical utility of the nomograms was measured by decision curve analyses (DCAs) and subgroups with different risk scores.

RESULTS: Tumor grade, mucinous adenocarcinoma, and age accounted for the first three largest proportion among the LNM nomogram scores (all, P < .001), whereas N stage, carcinoembryonic antigen (CEA), and tumor size occupied the largest percentage in DM nomogram (all, P < .001). OS nomogram was formulated to visually to predict 3-, 5-, and 10- year overall survivals for patients with T1 CRC. The calibration curves showed an effectively predictive accuracy of prediction nomograms, of which the C-index were 0.666, 0.874, and 0.760 for good discrimination, respectively. DCAs and risk subgroups revealed the clinical effectiveness of these nomograms.

CONCLUSIONS: Novel population-based nomograms for T1 CRC patients could objectively and accurately predict the risk of LNM and DM, as well as OS for different stages. These predictive tools may help clinicians to make individual clinical decisions, before clinical management.