Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network.

Journal: Scientific reports
PMID: 34650065

Abstract

Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resnet50 deep convolutional neural network was fine-tuned. The results showed that the averaged area under the receiver operating characteristic curve (AUC) of discriminating malignant from benign ones were 0.9789 (benign vs. TN), 0.9689 (benign vs. NTN). To discriminate TN from NTN breast cancer, the AUC was 0.9000, the accuracy was 88.89%, the sensitivity was 87.5%, and the specificity was 90.00%. It showed that the computer-aided system based on DCNN is expected to be a promising noninvasive clinical tool for ultrasound diagnosis of TN breast cancer.

Authors

  • Heng Ye
    The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China.
  • Jing Hang
    Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
  • Meimei Zhang
    The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China.
  • Xiaowei Chen
    School of Elderly Care Services and Management, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
  • Xinhua Ye
    Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. jsaume@126.com.
  • Jie Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Weixin Zhang
    Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
  • Di Xu
    School of Chemistry and Chemical Engineering, Chongqing University of Science & Technology, Chongqing, 401331, China. xdcq86@163.com.
  • Dong Zhang
    Institute of Acoustics, Nanjing University, Nanjing 210093, China.

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