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Related Experiment Video

Updated: Jun 18, 2025

Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats

Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats

Published on: October 16, 2010

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches.

João Mendes¹, Nuno Matela¹

¹Faculdade de Ciências, Instituto de Biofísica e Engenharia Biomédica, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

Journal of Imaging

|July 31, 2024

Summary

This summary is machine-generated.

Machine and deep learning methods show promise for predicting breast cancer risk using mammogram texture features. These approaches can stratify women into risk groups, aiding early diagnosis and reducing mortality.

Keywords:

breast cancer deep learning machine learning mammography risk assessment texture

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of mortality in women worldwide.
Early diagnosis through risk stratification can significantly reduce mortality rates.
Mammography is a key screening tool for breast cancer detection.

Purpose of the Study:

To review studies utilizing texture features from mammograms and machine learning for breast cancer risk assessment.
To analyze deep learning methodologies applied to breast cancer risk prediction.
To evaluate the effectiveness of AI-driven approaches in breast cancer risk analysis.

Main Methods:

Systematic review of research articles.
Analysis of texture feature extraction from mammograms.
Evaluation of machine learning and deep learning algorithms for risk assessment.
Comparison of methodologies and reported results.

Main Results:

Both machine learning and deep learning models demonstrate promising results in breast cancer risk analysis.
AI methodologies can effectively stratify women into different risk groups.
AI approaches can predict individual breast cancer risk scores with notable accuracy.

Conclusions:

Machine and deep learning offer powerful tools for breast cancer risk assessment.
Further research is needed to integrate these AI methodologies into clinical practice for improved patient outcomes.
AI-based risk prediction holds potential for enhancing breast cancer screening programs.