Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

Journal: European radiology experimental
Published Date: Jun 7, 2023

Abstract

BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the applicability of deep learning-based anomaly detection to identify anomalous changes in negative breast CE-MRI screens associated with future lesion emergence.

Authors

  • Bianca Burger
    Department of Biomedical Imaging and Image-Guided Therapy, Division of Computational Imaging Research (CIR), Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
  • Maria Bernathova
    Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria.
  • Philipp Seeböck
    Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
  • Christian F Singer
    Department of Obstetrics and Gynecology, and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
  • Thomas H Helbich
    Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy.
  • Georg Langs
    Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab, Medical University of Vienna Vienna Austria.

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