Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 10, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Statistical evaluation of a fully automated mammographic breast density algorithm.

Mohamed Abdolell1, Kaitlyn Tsuruda, Gerry Schaller

  • 1Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada B3H 2Y9. mo@dal.ca

Computational and Mathematical Methods in Medicine
|June 6, 2013
PubMed
Summary

This study validates an automated mammographic density algorithm, finding its accuracy comparable to radiologist assessments. The algorithm shows potential for reliable breast cancer risk stratification in clinical practice.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Diagnostic Yield of MRI-Transrectal US Fusion Prostate Biopsy in Patients With Suspected Prostate Cancer.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2025
Same author

Validation of R2* magnetic resonance imaging for quantifying secondary iron overload in pediatric patients.

Diagnostic and interventional radiology (Ankara, Turkey)·2025
Same author

Artificial Intelligence for Assessment of Digital Mammography Positioning Reveals Persistent Challenges.

Journal of breast imaging·2025
Same author

0.5T MRI as a competitor to CT for sinus imaging.

Scientific reports·2024
Same author

Time trends in systemic treatment for patients with metastatic prostate cancer: a national cohort study.

Acta oncologica (Stockholm, Sweden)·2023
Same author

Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2022

Area of Science:

  • Radiology
  • Medical Imaging
  • Biostatistics

Background:

  • Mammographic breast density is crucial for breast cancer risk assessment.
  • Current visual density assessments by radiologists show weaker risk associations than quantitative methods.
  • Area-based, quantitative mammographic density measurements offer improved risk prediction.

Purpose of the Study:

  • To statistically evaluate a fully automated, area-based mammographic density measurement algorithm.
  • To compare the algorithm's performance against a radiologist-derived reference standard.
  • To assess the algorithm's potential for clinical application in informing breast cancer risk.

Main Methods:

  • Five radiologists provided visual density estimates (5% increments) for 138 mammographic views.

More Related Videos

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: May 10, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

  • The median of radiologist estimates served as the reference standard.
  • Statistical analyses included inter-rater reliability (ICC), Bland-Altman plots, scatter plots, and box plots to evaluate the algorithm.

    • Main Results:

    • Excellent agreement was observed among radiologists (ICC = 0.884) and between the algorithm and the reference standard (ICC = 0.862).
    • The algorithm demonstrated a slight positive bias (+1.86%) compared to the reference standard.
    • While the algorithm showed some over/underestimation at density extremes, 95% of assessments fell within one BI-RADS category of the reference standard.

    Conclusions:

    • The automated mammographic density algorithm demonstrates excellent agreement with expert radiologist assessments.
    • Statistical evaluation confirms the algorithm's reliability and potential for clinical use.
    • This quantitative approach can enhance mammographic density measures for improved breast cancer risk assessment.