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

Our study identified key predictive factors for DLNM and elucidated significant prognostic indicators for HCC patients with DLNM. These findings provide clinicians with valuable tools to accurately identify high-risk individuals for DLNM and conduct more precise risk stratification for this patient subgroup, potentially improving management strategies and patient outcomes.

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Publication Details

Journal
The Journal of Gene Medicine
Published
2024-08-26
DOI
https://doi.org/10.1002/jgm.3732
Citations
4
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
Field-Weighted Citation Impact
1.48

Funders

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article

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

Yahui Liu, Lei Huang, Jiaxuan Sun
4 citations
The Journal of Gene Medicine
Hepatocellular Carcinoma Treatment and Prognosis
1.48
article

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

Yahui Liu, Lei Huang, Jiaxuan Sun
article en
4 citations

Abstract

Our study identified key predictive factors for DLNM and elucidated significant prognostic indicators for HCC patients with DLNM. These findings provide clinicians with valuable tools to accurately identify high-risk individuals for DLNM and conduct more precise risk stratification for this patient subgroup, potentially improving management strategies and patient outcomes.

The Journal of Gene MedicineVol. 26(9)
Jilin University (CN), First Hospital of Jilin University (CN)
National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 18%
Hepatocellular Carcinoma Treatment and Prognosis
1.48
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