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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Disk Harmonic Mapping of Cranial Surfaces for Fracture Visualization.

IEEE open journal of engineering in medicine and biology·2026
Same author

Breast Cancer Detection and Sub-typing Using Subtraction of Temporally Sequential Mammograms and Machine Learning: Assessment of Invasiveness and Tumor Grade.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Clinical and experimental treatment of advanced melanoma with a focus on immunotherapy.

Clinical and experimental immunology·2025
Same author

Subtraction of Temporally Sequential Digital Mammograms: Enhancing the Detection and Classification of Malignant Masses in Breast Imaging.

IEEE open journal of engineering in medicine and biology·2025
Same author

CD8<sup>+</sup> T cell-derived CD40L mediates noncanonical cytotoxicity in CD40-expressing cancer cells.

Science advances·2025
Same author

Dupilumab for the Treatment of Cutaneous Immune-Related Adverse Events: A Systematic Review.

International journal of dermatology·2025

Related Experiment Video

Updated: Aug 16, 2025

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

43.0K

A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms.

Kosmia Loizidou1, Galateia Skouroumouni2, Christos Nikolaou3

  • 1KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 2109, Cyprus.

Tomography (Ann Arbor, Mich.)
|December 22, 2022
PubMed
Summary

This review explores using prior mammograms to improve breast cancer detection. Analyzing changes over time in sequential mammogram pairs enhances computer-aided diagnosis (CAD) systems for more accurate malignancy identification.

Keywords:
breast cancercomputer-aided detectionmachine learningmammographyreviewsequential mammograms

More Related Videos

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.4K
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

22.6K

Related Experiment Videos

Last Updated: Aug 16, 2025

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

43.0K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.4K
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

22.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Mammography is crucial for breast cancer screening, relying on radiologists comparing current and prior images to detect suspicious changes.
  • Visual assessment of mammograms is complex, leading to the development of Computer-Aided Diagnosis (CAD) systems.
  • Current CAD systems often analyze only the most recent mammogram, potentially missing valuable temporal information.

Purpose of the Study:

  • To review methods for emulating the radiological approach of comparing sequential mammogram pairs for breast abnormality analysis.
  • To highlight the importance of incorporating prior mammographic views into CAD systems for improved breast cancer detection.
  • To provide a comprehensive overview of temporal analysis techniques in mammography for CAD development.

Main Methods:

  • Review of studies comparing radiologist performance with and without prior mammographic views.
  • Presentation of image registration techniques applicable to mammography.
  • Summary of studies employing temporal analysis or subtraction methods on sequential mammograms.
  • Description of open-access mammography datasets for research.

Main Results:

  • Prior mammographic views are essential for accurate interpretation and can significantly aid radiologists.
  • Image registration is a key technique for aligning sequential mammograms for comparison.
  • Temporal analysis methods, including subtraction, can effectively highlight changes indicative of malignancy.
  • The review consolidates existing research and datasets, paving the way for advanced CAD systems.

Conclusions:

  • Utilizing prior mammographic information in CAD systems mimics expert radiological practice and improves diagnostic accuracy.
  • Further development in temporal analysis and image registration for sequential mammograms is crucial for next-generation CAD tools.
  • This review serves as an introduction and guide for future research in breast cancer CAD systems leveraging temporal data.