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Objective:  The purpose of this study was to identify predictive factors for lymph node metastasis (LNM) in pT1 stage colorectal cancer (CRC) patients.   Methods:  From the Surveillance, Epidemiology, and End Results (SEER) database, 2,697 consecutive pT1 stage patients who underwent surgical resection were retrospectively reviewed. Predictive factors for LNM were identified by the univariate and multivariate logistic regression analysis. The Kaplan-Meier curves and multivariate Cox regression analysis were used to evaluate the relationships between LNM and overall survival (OS) as well as cancer specific survival (CSS) of pT1 stage CRC patients.   Results:  The prevalence of LNM in pT1 stage CRC patients was 15.2% (410/2,697). Patient age &amp;lt;60 years (OR:1.869, 95% CI: 1.505–2.321,  p  &amp;lt; 0.001), poorly differentiated or mucinous or signet ring cell adenocarcinoma (OR:2.075, 95% CI: 1.584–2.717,  p  &amp;lt; 0.001), elevated carcinoembryonic antigen (CEA) level (OR:1.343, 95% CI: 1.022–1.763,  p  = 0.033) and perineural invasion (PNI) (OR:6.212, 95% CI: 3.502–11.017,  p  &amp;lt; 0.001) were significantly associated with LNM in pT1 stage patients. The survival analysis demonstrated that pT1 stage patients with LNM had a worse OS (5-year OS: 82.2% vs 88.7%,  p  = 0.020) and CSS (5-year CSS: 74.9% vs 81.5%,  p  = 0.041) than those without lymph node metastasis. Lymph node metastasis was an independent predictor of poor OS (HR: 1.543, 95% CI: 1.156–2.060,  p  = 0.003) and CSS (HR: 1.614, 95% CI: 1.121–2.324,  p  = 0.010) for pT1 stage colorectal cancer patients.   Conclusion:  Age, differentiation type, CEA level and perineural invasion were independent predictive factors for LNM in pT1 stage CRC patients. These findings might provide further risk stratification for pT1 stage patients and help clinicians identify high-risk individuals.

pathology and oncology research/pathology oncology research

Identification of Predictive Factors for Lymph Node Metastasis in pT1 Stage Colorectal Cancer Patients: A Retrospective Analysis Based on the Population Database