Machine learning for diagnostic ultrasound of triple-negative breast cancer.

Journal: Breast cancer research and treatment
PMID: 30343454

Abstract

PURPOSE: Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine learning with quantitative ultrasound image features for the diagnosis of TN breast cancer.

Authors

  • Tong Wu
    National Clinical Research Center for Obstetrical and Gynecological Diseases Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan China.
  • Laith R Sultan
    Department of Radiology, University of Pennsyvlania, Philadelphia, PA, USA.
  • Jiawei Tian
    Department of Ultrasound, Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin City, 150086, Heilongjiang Province, People's Republic of China. jwtian2004@163.com.
  • Theodore W Cary
    Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, 168B John Morgan Building, 3620 Hamilton Walk, Philadelphia, PA, 19104, USA.
  • Chandra M Sehgal
    Department of Radiology, University of Pennsyvlania, Philadelphia, PA, USA.

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