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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1398685
This article is part of the Research Topic Application of Bioinformatics, Machine Learning, and Artificial Intelligence to Improve Diagnosis, Prognosis and Treatment of Cancer View all 6 articles

Machine Learning to Predict Distant Metastasis and Prognostic Analysis in Moderately Differentiated Gastric Adenocarcinoma Patients: Novel Focus on Lymph Node Indicators

Provisionally accepted
Kangping Yang Kangping Yang 1Jiaqiang Wu Jiaqiang Wu 2Tian Xu Tian Xu 3*Yuepeng Zhou Yuepeng Zhou 1*Wenchun Liu Wenchun Liu 4*Liang Yang Liang Yang 1*
  • 1 Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
  • 2 First Affiliated Hospital of Chinese PLA General Hospital, Beijing, Beijing Municipality, China
  • 3 Jiangxi Province Hospital of Integrated Chinese and Western Medicine, Nanchang, Jiangxi Province, China
  • 4 Second Department of Internal Medicine, Anfu County People's Hospital, Anfu, China

The final, formatted version of the article will be published soon.

    Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improve individualized treatment. The specific efficacy of several lymph node indicators in predicting distant metastasis (DM) and patient prognosis in MDGA remains obscure.We collected data from MDGA patients from the SEER database from 2010 to 2019. Additionally, we collected data from MDGA patients in China. We used 9 machine learning algorithms to predict DM. Subsequently, we used Cox regression analysis to determine the risk factors affecting overall survival (OS) and cancer-specific survival (CSS) in DM patients and constructed nomograms. Furthermore, we used logistic regression and Cox regression analyses to assess the specific impact of 6 lymph node indicators on DM incidence and patient prognosis.We collected data from 5377 MDGA patients from the SEER database and 109 MDGC patients from hospitals. T stage, N stage, tumor size, primary site, number of positive lymph nodes, and chemotherapy were identified as independent risk factors for DM. The random forest prediction model had the best overall predictive performance (AUC=0.919). T stage, primary site, chemotherapy and the number of regional lymph nodes were identified as prognostic factors for OS. Moreover, T stage, number of regional lymph nodes, primary site and chemotherapy were also influential factors for CSS. The nomograms showed good predictive value and stability in predicting 1-year, 3-year, and 5-year OS and CSS in DM patients. Additionally, the log odds of a metastatic lymph node and the number of negative lymph nodes may be risk factors for DM, while the regional lymph node ratio and the number of regional lymph nodes are prognostic factors for OS.The random forest prediction model accurately identified high-risk populations, and we established OS and CSS survival prediction models for MDGA patients with DM. Our hospital samples demonstrated different characteristics of lymph node indicators in terms of distant metastasis and prognosis.

    Keywords: Moderately differentiated gastric adenocarcinoma, prognosis, nomogram, lymph node indicators, distant metastasis, machine learning

    Received: 10 Mar 2024; Accepted: 29 Aug 2024.

    Copyright: © 2024 Yang, Wu, Xu, Zhou, Liu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Tian Xu, Jiangxi Province Hospital of Integrated Chinese and Western Medicine, Nanchang, 330003, Jiangxi Province, China
    Yuepeng Zhou, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
    Wenchun Liu, Second Department of Internal Medicine, Anfu County People's Hospital, Anfu, China
    Liang Yang, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.