Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.

Journal: Frontiers in endocrinology
PMID: 40084140

Abstract

OBJECTIVE: This study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.

Authors

  • Si-Rui Wang
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Feng Tian
    Bioinformatics Graduate Program, and Department of Biomedical Engineering, Boston. University, 24 Cummington Mall, Boston, MA 02215, USA.
  • Tong Zhu
  • Chun-Li Cao
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Jin-Li Wang
  • Wen-Xiao Li
    The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China.
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Ji-Xue Hou
    The Thyroid and Breast Surgery Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.

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