A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction.

Journal: Academic radiology
PMID: 39799013

Abstract

RATIONALE AND OBJECTIVES: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imaging Reporting and Data System (BI-RADS) ≥4A, most of which are 4A/4B. Contrast-enhanced mammography (CEM) may improve biopsy outcome prediction for this subpopulation, but machine learning-based analysis of CEM is largely unexplored. We aim to develop a machine learning-based analysis of CEM using computer-extracted radiomics and radiologist-assessed descriptors to predict breast biopsy outcomes of BI-RADS 4A/4B/4C or 5 lesions.

Authors

  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Priya Patel
    Department of Kinesiology, Michigan State University, East Lansing, Michigan, United States of America.
  • Dooman Arefan
    Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States.
  • Margarita Zuley
    Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Jules Sumkin
  • Shandong Wu
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.

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