Cargando…

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

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 1...

Descripción completa

Detalles Bibliográficos
Autores principales: Lai, Yongkang, Xie, Junfeng, Yin, Xiaojing, Lai, Weiguo, Tang, Jianhua, Du, Yiqi, Li, Zhaoshen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918112/
https://www.ncbi.nlm.nih.gov/pubmed/36769809
http://dx.doi.org/10.3390/jcm12031160
_version_ 1784886532193124352
author Lai, Yongkang
Xie, Junfeng
Yin, Xiaojing
Lai, Weiguo
Tang, Jianhua
Du, Yiqi
Li, Zhaoshen
author_facet Lai, Yongkang
Xie, Junfeng
Yin, Xiaojing
Lai, Weiguo
Tang, Jianhua
Du, Yiqi
Li, Zhaoshen
author_sort Lai, Yongkang
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9918112
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99181122023-02-11 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 J Clin Med Article 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. MDPI 2023-02-01 /pmc/articles/PMC9918112/ /pubmed/36769809 http://dx.doi.org/10.3390/jcm12031160 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lai, Yongkang
Xie, Junfeng
Yin, Xiaojing
Lai, Weiguo
Tang, Jianhua
Du, Yiqi
Li, Zhaoshen
Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach
title Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach
title_full Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach
title_fullStr Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach
title_full_unstemmed Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach
title_short Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach
title_sort survival outcome of gastric signet ring cell carcinoma based on the optimal number of examined lymph nodes: a nomogram- and machine-learning-based approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918112/
https://www.ncbi.nlm.nih.gov/pubmed/36769809
http://dx.doi.org/10.3390/jcm12031160
work_keys_str_mv AT laiyongkang survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach
AT xiejunfeng survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach
AT yinxiaojing survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach
AT laiweiguo survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach
AT tangjianhua survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach
AT duyiqi survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach
AT lizhaoshen survivaloutcomeofgastricsignetringcellcarcinomabasedontheoptimalnumberofexaminedlymphnodesanomogramandmachinelearningbasedapproach