Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

Journal: The journal of gene medicine

PMID: 39188041

Abstract

OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for this cohort.

Authors

Jiaxuan Sun

Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, China.
Lei Huang

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Yahui Liu

Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.

Keywords

Adult Aged alpha-Fetoproteins Carcinoma, Hepatocellular Female Humans Liver Neoplasms Lymphatic Metastasis Machine Learning Male Middle Aged Neoplasm Staging Nomograms Prognosis Proportional Hazards Models Retrospective Studies SEER Program

External Resources

View on PubMed Access via DOI PubMed (39188041)

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

Abstract

Authors

Keywords

External Resources

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