Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.

Journal: Breast cancer research : BCR
PMID: 40369585

Abstract

BACKGROUND: This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, particularly in cases with discordant US and MG Breast Imaging Reporting and Data System (BI-RADS) classifications.

Authors

  • Ziting Xu
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China.
  • Shengzhou Zhong
    School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
  • Yang Gao
    State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100050, China.
  • Jiekun Huo
    Department of Imaging, Zengcheng Branch of Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Weimin Xu
    Institute of Agricultural Products Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, PR China.
  • Weijun Huang
  • Xiaomei Huang
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China; Southern Medical University, Guangzhou, Guangdong Province, PR China.
  • Chifa Zhang
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • JianQiao Zhou
    Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, No. 197, Rui Jin 2nd Road, Shanghai, 200025, China. zhousu30@126.com.
  • Qing Dan
    Shenzhen Key Laboratory for Drug Addiction and Medication Safety, Department of Ultrasound, Institute of Ultrasonic Medicine, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China.
  • Lian LI
  • Zhouyue Jiang
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Ting Lang
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China.
  • Shuying Xu
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Jiayin Lu
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Ge Wen
    Medical Imaging Center, Nanfang Hospital, Southern Medical University, 1023 Shatai South Road, Baiyun District, Guangzhou, Guangdong, China. m13360022166@163.com.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Yingjia Li
    Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China. lyjia@smu.edu.cn.

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