Risk factors for lymph node metastasis in rectal neuroendocrine tumors: A recursive partitioning analysis based on multicenter data

医学 逻辑回归 入射(几何) 置信区间 内科学 递归分区 淋巴结转移 单变量分析 多元分析 神经内分泌肿瘤 单变量 肿瘤科 转移 淋巴结 比例危险模型 多元统计 癌症 统计 光学 物理 数学
作者
Ye Wang,Yiyi Zhang,Hexin Lin,Meifang Xu,Xin Zhou,Jinfu Zhuang,Yuanfeng Yang,Bin Chen,Xing Liu,Guoxian Guan
出处
期刊:Journal of Surgical Oncology [Wiley]
卷期号:124 (7): 1098-1105 被引量:12
标识
DOI:10.1002/jso.26615
摘要

Abstract Background The well‐differentiated rectal neuroendocrine tumors (RNETs) can also have lymph node metastasis (LNM). Large multicenter data were reviewed to explore the risk factors for LNM in RNETs. Further, we developed a model to predict the risk of LNM in RNETs. Methods In total, 223 patients with RNETs from the Fujian Medical University Union Hospital, the First Affiliated Hospital of Fujian Medical University, and the First Affiliated Hospital of Xiamen University were retrospectively enrolled. Logistic regression analysis was performed to study the factors affecting LNM, and recursive partitioning analysis (RPA) was performed to stratify the risk of LNM. Results Among the 223 patients diagnosed with RNETs, the incidence of LNM was 10.8%. Univariate and multivariate regression analyses revealed that tumor size, World Health Organization (WHO) grade, and depth of tumor invasion were independent risk factors for LNM ( p < 0.05). The area under the curve was 0.948 (95% confidence interval: 0.890–1.000). Furthermore, the incidence of LNM in patients divided into low‐ and high‐risk groups according to RPA was 1.1% and 56.4%, respectively. Conclusion Compared with tumor size, the depth of tumor invasion and WHO grade are more important factors in predicting LNM. Then, we developed a model based on RPA to predict the risk of LNM in RNETs and identify patients who are suitable for local resection.
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