Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators.

Journal: Frontiers in immunology
PMID: 39364413

Abstract

BACKGROUND: 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.

Authors

  • Kangping Yang
    Department of Gastroenterological Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
  • Jiaqiang Wu
    School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenyang, China.
  • Tian Xu
    School of Civil and Architecture Engineering, Xi'an Technological University, Xi'an 710032, China.
  • Yuepeng Zhou
    Department of Gastroenterological Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
  • Wenchun Liu
    The Second Department of Internal Medicine, Anfu People's Hospital, Anfu, Jiangxi, China.
  • Liang Yang

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