Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.

Journal: European radiology experimental
Published Date: Nov 1, 2019

Abstract

BACKGROUND: Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.

Authors

  • Magda Marcon
  • Alexander Ciritsis
    From the *Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
  • Cristina Rossi
  • Anton S Becker
    From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
  • Nicole Berger
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland.
  • Moritz C Wurnig
  • Matthias W Wagner
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
  • Thomas Frauenfelder
  • Andreas Boss

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