Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators
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.
Authors
- Kangping Yang (ORCID: https://orcid.org/0000-0003-1298-0846)
- Yuepeng Zhou (ORCID: https://orcid.org/0000-0003-2428-4987)
- Jiaqiang Wu (ORCID: https://orcid.org/0009-0002-8289-7853)
- Tian Xu (ORCID: https://orcid.org/0000-0003-4952-5361)
- Liang Yang (ORCID: https://orcid.org/0009-0008-6210-4426)
- Wenchun Liu
Institutions
- Jiangxi University of Traditional Chinese Medicine (CN)
- Nanchang University (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Integrated Chinese Medicine (China) (HK)
- Second Affiliated Hospital of Nanchang University (CN)
- Second Hospital of Nanchang (CN)
- Chinese People's Liberation Army (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-09-19
- DOI
- https://doi.org/10.3389/fimmu.2024.1398685
- Citations
- 11
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 4.61
Funders
- National Natural Science Foundation of China