Population-based analysis on predictors for lymph node metastasis in T1 colon cancer

基于人群的T1期结肠癌淋巴结转移预测因素分析

阅读:1

Abstract

BACKGROUND: In this study, we aimed to identify independent predictive factors for lymph node metastasis (LNM) in T1 colon cancer. METHODS: Data of 8056 eligible patients were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database during 2004-2012. We performed logistic regression analysis to identify predictive factors for LNM. Both unadjusted and adjusted Cox regression analyses were used to determine the association between LNM and patient survival. Finally, we used competing risks analysis and the cumulative incidence function (CIF) to further confirm the prognostic role of LNM in cancer-specific survival (CSS). RESULTS: The overall risk of LNM in patients with T1 colon cancer was 12.0% (N = 967). Adjusted logistic regression models revealed that mucinous carcinoma [odds ratio (OR) = 2.26, P < 0.001], moderately differentiated (OR 1.74, P < 0.001), poorly differentiated (OR 5.16, P < 0.001), and undifferentiated carcinoma (OR 3.01, P = 0.003); older age (OR 0.66, P < 0.001 for age 65-79 years, OR 0.44, P < 0.001 for age over 80 years); and carcinoma located in the ascending colon (OR 0.77, P = 0.018) and sigmoid colon (OR 1.24, P = 0.014) were independent predictive factors for LNM. Adjusted Cox regression analysis showed that positive lymph node involvement was significantly associated with CSS [hazard ratio (HR) = 3.02, P < 0.001], which was further robustly confirmed using a competing risks model and the CIF. CONCLUSIONS: This population-based study showed that mucinous carcinoma, tumor grade, age, and primary tumor location were independent predictive factors for LNM in T1 colon cancer. The risk of LNM should be carefully evaluated in patients with T1 colon cancer, before clinical management.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。