A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.

Journal: Radiation oncology (London, England)
PMID: 38802938

Abstract

BACKGROUND: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.

Authors

  • Ranze Cai
    Department of Neurosurgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian Province, 361006, China.
  • Li Deng
    Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China.
  • Hua Zhang
    School of Clinical Medicine, Hangzhou Medical College, Hangzhou, China.
  • Hongwei Zhang
    Jiangsu Provincial Key Laboratory for TCM Evaluation and Translational Development, China Pharmaceutical University, Nanjing, Jiangsu 211198, China.
  • Qian Wu
    China Electric Power Research Institute, Beijing, China.

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