Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.

Journal: BMC cancer
Published Date: Oct 2, 2020

Abstract

BACKGROUND: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.

Authors

  • Sihua Niu
    Department of Ultrasound, Peking University People's Hospital, Beijing, 100044, China.
  • Jianhua Huang
    Hunan University of Traditional Chinese Medicine College of Pharmacy, Changsha 410028, China.
  • Jia Li
    Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Guangdong, 528400, PR China; School of Pharmacy, Zunyi Medical University, Zunyi, 563000, PR China; National Center for Drug Screening, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, PR China.
  • Xueling Liu
    Department of Ultrasound, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, Guangxi Zhuang Autonomous Region, China.
  • Dan Wang
    Guangdong Pharmaceutical University Guangzhou Guangdong China.
  • Ruifang Zhang
    Department of Ultrasound, Zhengzhou University First Affiliated Hospital, Zhengzhou, 450052, Henan Province, China.
  • Yingyan Wang
    Department of Ultrasound, Southeast University Zhongda Hospital, Nanjing, 210009, Jiangsu Province, China.
  • Huiming Shen
    Department of Ultrasound, Southeast University Zhongda Hospital, Nanjing, 210009, Jiangsu Province, China.
  • Min Qi
    Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.
  • Yi Xiao
    Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
  • Mengyao Guan
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang Province, China.
  • Haiyan Liu
    Department of Neurology, Xinyang Central Hospital, Xinyang 464000, China.
  • Diancheng Li
    Department of Ultrasound, Peking University People's Hospital, Beijing, 100044, China.
  • Feifei Liu
  • Xiuming Wang
    Department of Ultrasound, Peking University People's Hospital, Beijing, 100044, China.
  • Yu Xiong
    Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Siqi Gao
    Department of Ultrasound, Peking University People's Hospital, Beijing, 100044, China.
  • Xue Wang
    Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening Biology Institute, Qilu University of Technology (Shandong Academy of Sciences) Jinan China.
  • Jiaan Zhu
    Department of Ultrasound, Peking University People's Hospital, Beijing, 100044, China. zhujiaan@pkuph.edu.cn.

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