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基于最佳检查淋巴结数量的胃印戒细胞癌生存结果:一种基于列线图和机器学习的方法

Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach.

作者信息

Lai Yongkang, Xie Junfeng, Yin Xiaojing, Lai Weiguo, Tang Jianhua, Du Yiqi, Li Zhaoshen

机构信息

Department of Gastroenterology, Ganzhou People's Hospital Affiliated to Nanchang University, Ganzhou 341000, China.

Department of Gastroenterology, Shanghai Changhai Hospital, Naval Medical University, Shanghai 200433, China.

出版信息

J Clin Med. 2023 Feb 1;12(3):1160. doi: 10.3390/jcm12031160.

Abstract

The optimal number of examined lymph nodes (ELNs) for gastric signet ring cell carcinoma recommended by National Comprehensive Cancer Network guidelines remains unclear. This study aimed to determine the optimal number of ELNs and investigate its prognostic significance. In this study, we included 1723 patients diagnosed with gastric signet ring cell carcinoma in the Surveillance, Epidemiology, and End Results database. X-tile software was used to calculate the cutoff value of ELNs, and the optimal number of ELNs was found to be 32 for adequate nodal staging. In addition, we performed propensity score matching (PSM) analysis to compare the 1-, 3-, and 5-year survival rates; 1-, 3-, and 5-year survival rates for total examined lymph nodes (ELNs < 32 vs. ELNs ≥ 32) were 71.7% vs. 80.1% ( = 0.008), 41.8% vs. 51.2% ( = 0.009), and 27% vs. 30.2% ( = 0.032), respectively. Furthermore, a predictive model based on 32 ELNs was developed and displayed as a nomogram. The model showed good predictive ability performance, and machine learning validated the importance of the optimal number of ELNs in predicting prognosis.

摘要

美国国立综合癌症网络指南推荐的胃印戒细胞癌检查淋巴结(ELN)的最佳数量仍不明确。本研究旨在确定ELN的最佳数量并探讨其预后意义。在本研究中,我们纳入了监测、流行病学和最终结果数据库中1723例诊断为胃印戒细胞癌的患者。使用X-tile软件计算ELN的截断值,发现充足的淋巴结分期时ELN的最佳数量为32个。此外,我们进行了倾向评分匹配(PSM)分析以比较1年、3年和5年生存率;总检查淋巴结(ELN < 32与ELN≥32)的1年、3年和5年生存率分别为71.7%对80.1%(P = 0.008)、41.8%对51.2%(P = 0.009)和27%对30.2%(P = 0.032)。此外,还开发了基于32个ELN的预测模型并以列线图展示。该模型显示出良好的预测能力,机器学习验证了ELN最佳数量在预测预后中的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aad0/9918112/a2251e6feb09/jcm-12-01160-g001.jpg

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