Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study

The prediction models based on machine learning (ML) algorithms and multiomics have shown good performance in predicting ALN metastasis, and RFM shows greater advantages compared to traditional GLRM. The findings of this study can help clinicians identify patients with higher risk of ALN metastasis and provide personalized perioperative management to assist preoperative decision-making and improve patient prognosis.

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Publication Details

Journal
International Journal of General Medicine
Published
2024-12-01
DOI
https://doi.org/10.2147/ijgm.s499238
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.81
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article

Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study

Leihua Shen, Zirui Ke, Jun Shao
2 citations
International Journal of General Medicine
Radiomics and Machine Learning in Medical Imaging
0.81
article

Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study

Leihua Shen, Zirui Ke, Jun Shao
article en
2 citations

Abstract

The prediction models based on machine learning (ML) algorithms and multiomics have shown good performance in predicting ALN metastasis, and RFM shows greater advantages compared to traditional GLRM. The findings of this study can help clinicians identify patients with higher risk of ALN metastasis and provide personalized perioperative management to assist preoperative decision-making and improve patient prognosis.

International Journal of General MedicineVol. Volume 17
Xian Central Hospital (CN), Hubei Cancer Hospital (CN), Huazhong University of Science and Technology (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 24%
Radiomics and Machine Learning in Medical Imaging
0.81
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