Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.

Journal: Nature biomedical engineering
Published Date: Apr 19, 2021

Abstract

The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound images has been hindered by inter-grader variability and high false positive rates and by deep-learning models that do not follow Breast Imaging Reporting and Data System (BI-RADS) standards, lack explainability features and have not been tested prospectively. Here, we show that an explainable deep-learning system trained on 10,815 multimodal breast-ultrasound images of 721 biopsy-confirmed lesions from 634 patients across two hospitals and prospectively tested on 912 additional images of 152 lesions from 141 patients predicts BI-RADS scores for breast cancer as accurately as experienced radiologists, with areas under the receiver operating curve of 0.922 (95% confidence interval (CI) = 0.868-0.959) for bimodal images and 0.955 (95% CI = 0.909-0.982) for multimodal images. Multimodal multiview breast-ultrasound images augmented with heatmaps for malignancy risk predicted via deep learning may facilitate the adoption of ultrasound imaging in screening mammography workflows.

Authors

  • Xuejun Qian
    Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
  • Jing Pei
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Hui Zheng
    Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China.
  • Xinxin Xie
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Lin Yan
    School of Computer Science and Technology, Xidian University, Xi'an, China.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Chunguang Han
    Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Xiang Gao
    Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China.
  • Hanqi Zhang
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Weiwei Zheng
    Key Laboratory of the Public Health Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China.
  • Qiang Sun
    Research Center for Agricultural and Sideline Products Processing, Henan Academy of Agricultural Sciences, 116 Park Road, Zhengzhou 450002, PR China.
  • Lu Lu
    China School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, China.
  • K Kirk Shung
    Department of Biomedical Engineering and NIH Resource Center for Medical Ultrasonic Transducer Technology, University of Southern California, Los Angeles, CA, 90089, USA.

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