Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
PMID: 38808580

Abstract

OBJECTIVE: This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer.

Authors

  • Si-Rui Wang
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Chun-Li Cao
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Ting-Ting Du
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Jin-Li Wang
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Wen-Xiao Li
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Ming Chen
    Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.

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