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Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach.
Lai, Yongkang; Xie, Junfeng; Yin, Xiaojing; Lai, Weiguo; Tang, Jianhua; Du, Yiqi; Li, Zhaoshen.
Afiliação
  • Lai Y; Department of Gastroenterology, Ganzhou People's Hospital Affiliated to Nanchang University, Ganzhou 341000, China.
  • Xie J; Department of Gastroenterology, Shanghai Changhai Hospital, Naval Medical University, Shanghai 200433, China.
  • Yin X; Department of Gastroenterology, Ganzhou People's Hospital Affiliated to Nanchang University, Ganzhou 341000, China.
  • Lai W; Department of Gastroenterology, Shanghai Changhai Hospital, Naval Medical University, Shanghai 200433, China.
  • Tang J; Department of Gastroenterology, Ganzhou People's Hospital Affiliated to Nanchang University, Ganzhou 341000, China.
  • Du Y; Department of Gastroenterology, Ganzhou People's Hospital Affiliated to Nanchang University, Ganzhou 341000, China.
  • Li Z; Department of Gastroenterology, Shanghai Changhai Hospital, Naval Medical University, Shanghai 200433, China.
J Clin Med ; 12(3)2023 Feb 01.
Article em En | MEDLINE | ID: mdl-36769809
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% (p = 0.008), 41.8% vs. 51.2% (p = 0.009), and 27% vs. 30.2% (p = 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.
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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: J Clin Med Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: J Clin Med Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China