Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

Journal: AJR. American journal of roentgenology
PMID: 29792725

Abstract

OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers.

Authors

  • Johannes Uhlig
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.
  • Annemarie Uhlig
    2 Department of Urology, University Medical Center Goettingen, Goettingen, Germany.
  • Meike Kunze
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.
  • Tim Beissbarth
    3 Department of Medical Statistics, University Medical Center Goettingen, Goettingen, Germany.
  • Uwe Fischer
    Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Yale School of Medicine, New Haven, CT, USA.
  • Joachim Lotz
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.
  • Susanne Wienbeck
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.

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