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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Related Experiment Video

Updated: May 12, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Deep Learning for Contrast Enhanced Mammography - A Systematic Review.

Vera Sorin1, Miri Sklair-Levy2, Benjamin S Glicksberg3

  • 1Department of Radiology, Mayo Clinic, Rochester, MN (V.S.).

Academic Radiology
|December 6, 2024
PubMed
Summary

Deep learning (DL) shows promise in improving contrast-enhanced mammography (CEM) for breast cancer diagnosis. Further prospective studies are needed to validate DL applications in clinical settings.

Keywords:
Breast cancerCEMCNNContrast-enhanced mammographyConvolutional neural networksDLRadiology

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Contrast-enhanced mammography (CEM) offers superior diagnostic performance over standard 2D mammography by providing both anatomical and functional breast imaging.
  • Deep learning (DL) presents an opportunity to further enhance the diagnostic capabilities of CEM.

Purpose of the Study:

  • To systematically review existing literature on deep learning applications for contrast-enhanced mammography.
  • To explore how DL models can augment the diagnostic potential of CEM.

Main Methods:

  • A systematic literature review following PRISMA guidelines, searching MEDLINE, Scopus, and Google Scholar up to April 2024.
  • Inclusion of original English-language studies evaluating DL algorithms for automatic CEM image analysis.
  • Quality assessment of included studies using the QUADAS-2 criteria.

Main Results:

  • Sixteen studies (2018-2024) primarily utilized Convolutional Neural Network (CNN) models.
  • DL algorithms were evaluated for lesion classification, detection, and segmentation, with varying AUCs (0.53-0.99) on retrospective datasets.
  • Models with attention mechanisms and combinations with radiomics showed improved classification; integration with radiologists enhanced performance.

Conclusions:

  • Deep learning holds potential to increase the diagnostic precision of CEM, though it remains in early research stages.
  • A limited number of retrospective studies exist, highlighting the need for more research on diverse DL algorithms.
  • Further prospective studies are crucial to evaluate DL application performance in real clinical settings.