Li Ruizhen, Li Xiaofen, Wang Yan, Chang Chen, Lv Wanrui, Li Xiaoying, Cao Dan
Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Abdominal Oncology Ward, Division of Medical Oncology, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Front Med (Lausanne). 2024 Sep 4;11:1383047. doi: 10.3389/fmed.2024.1383047. eCollection 2024.
The identification of risk factors for regional lymph node (r-LN) metastasis in rectal neuroendocrine tumors (R-NETs) remains challenging. Our objective was to investigate the risk factors associated with patients diagnosed with R-NETs exhibiting r-LN metastasis.
Patient information was obtained from the Surveillance, Epidemiology, and End Results (SEER) database, complemented by data from the West China Hospital (WCH) databases. The construction cohort comprised patients diagnosed with R-NETs from the SEER database, while cases from the WCH database were utilized as the validation cohort. A novel nomogram was developed to predict the probability of r-LN metastasis, employing a logistic regression model.
Univariate analysis identified four independent risk factors associated with poor r-LN metastasis: age (HR = 1.027, < 0.05), grade (HR = 0.010, < 0.05), T stage (HR = 0.010, < 0.05), and tumor size (HR = 0.005, p < 0.05). These factors were selected as predictors for nomogram construction.
The novel nomogram serves as a reliable tool for predicting the risk of r-LN metastasis, providing clinicians with valuable assistance in identifying high-risk patients and tailoring individualized treatments.
直肠神经内分泌肿瘤（R-NETs）区域淋巴结（r-LN）转移危险因素的识别仍然具有挑战性。我们的目的是研究与诊断为R-NETs且出现r-LN转移的患者相关的危险因素。
患者信息来自监测、流行病学和最终结果（SEER）数据库，并辅以华西医院（WCH）数据库的数据。构建队列包括来自SEER数据库中诊断为R-NETs的患者，而WCH数据库中的病例用作验证队列。采用逻辑回归模型开发了一种新的列线图来预测r-LN转移的概率。
单因素分析确定了与r-LN转移不良相关的四个独立危险因素：年龄（HR = 1.027，<0.05）、分级（HR = 0.010，<0.05）、T分期（HR = 0.010，<0.05）和肿瘤大小（HR = 0.005，p < 0.05）。这些因素被选为列线图构建的预测因子。
新的列线图是预测r-LN转移风险的可靠工具，为临床医生识别高危患者和制定个体化治疗提供了有价值的帮助。
