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Volume 12 Issue 6
Nov.  2021
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GAO Yuanjing, ZHU Qingli, JIANG Yuxin. Research Progress of Ultrasound Radiomics in Predicting Axillary Lymph Node Metastasis of Breast Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 989-993. DOI: 10.12290/xhyxzz.2021-0187
Citation: GAO Yuanjing, ZHU Qingli, JIANG Yuxin. Research Progress of Ultrasound Radiomics in Predicting Axillary Lymph Node Metastasis of Breast Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 989-993. DOI: 10.12290/xhyxzz.2021-0187
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Research Progress of Ultrasound Radiomics in Predicting Axillary Lymph Node Metastasis of Breast Cancer

  • Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

Funds: 

CAMS Innovation Fund for Medical Science 2020-I2M-C & T-B-033

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  • Corresponding author:

    ZHU Qingli  Tel: 86-10-69155494, E-mail: zqlpumch@126.com

  • Received Date: February 07, 2021
  • Accepted Date: March 04, 2021
  • Available Online: October 24, 2021
  • Issue Publish Date: November 29, 2021
    Graphical Abstract
    • Abstract
  • Breast cancer, as the most common malignant tumor in women worldwide, has become the focus of global attention. Axillary lymph node tumor burden is an important prognostic indicator of it. Ultrasound is the most commonly used imaging method, but its sensitivity is not high, especially for the diagnosis of small and micro lymph node metastasis. In recent years, the emerging Radiomics in machine learning field has been widely used in the field of medical imaging. Because it can extract high-level information of images that is difficult to be recognized by human eyes, it has been used to establish clinical prediction models. This paper introduced the value of preoperative sonography, sketched Radiomics, and summarized the research progress of this method in predicting lymph node metastasis of breast cancer. This new method is expected to provide a reliable basis for individualized and accurate diagnosis and treatment of breast cancer.
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    • References (40)
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