Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers.

Journal: Ultrasound in medicine & biology
PMID: 39627056

Abstract

OBJECTIVE: Early and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic features extracted from ultrasound (US) images combined with machine learning (ML) methods in predicting ALNM to improve diagnostic accuracy and patient prognosis.

Authors

  • Meiying Yan
    Department of Ultrasound, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Cancer Hospital of University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, People's Republic of China.
  • Dilin He
    Department of Ultrasound, The First People's Hospital of Fuyang District, Hangzhou City, Zhejiang, PR China.
  • Yu Sun
    Department of Neurology, China-Japan Friendship Hospital, Beijing, China.
  • Long Huang
    a Institute of Functional Molecules, College of Chemistry and Life Science , Chengdu Normal University , Chengdu , China.
  • Linli Cai
    Department of Ultrasonic Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an City, Shaanxi, PR China.
  • Chen Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Jincao Yao
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Xiangyang Li
    Department of Toxicology and Hygienic Chemistry, School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China.
  • Hongping Song
    Department of Ultrasonic Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an City, Shaanxi, PR China.
  • Chen Yang
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.

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