Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

Journal: International journal of surgery (London, England)
Published Date:

Abstract

OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts.

Authors

  • Yaping Yang
    AiLife Diagnostics, Pearland, TX, USA.
  • Ying Zhong
    Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou.
  • Junwei Li
    Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou.
  • Jiahao Feng
    Cellsvision (Guangzhou) Medical Technology Inc., People's Republic of China.
  • Chang Gong
    Breast Tumor Center.
  • Yunfang Yu
    Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Cancer Pathogenesis and Precision Diagnosis and Treatment, Joint Big Data Laboratory, Department of Medical Oncology, Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University, Shanwei, China; Institute for AI in Medicine and faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, China; Department of Breast Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, China.
  • Yue Hu
    Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Ran Gu
  • Hongli Wang
    Breast Tumor Center.
  • Fengtao Liu
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
  • Jingsi Mei
    Breast Tumor Center.
  • Xiaofang Jiang
    National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
  • Jin Wang
    Cells Vision (Guangzhou) Medical Technology Inc., Guangzhou, China. Electronic address: wangjin@cellsvision.com.
  • Qinyue Yao
    Cells Vision (Guangzhou) Medical Technology Inc., Guangzhou, China.
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Qiang Liu
    Blood Transfusion Laboratory, Jiangxi Provincial Blood Center Nanchang 330052, Jiangxi, China.
  • Herui Yao
    Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: yaoherui@mail.sysu.edu.cn.