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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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Cells and tissues must meticulously coordinate their activities for the normal functioning of the human body. Therefore, they exhibit socially responsible behavior - resting, growing, dividing, differentiating, or dying - for the organism’s benefit. Cancer arises when cells divide uncontrollably and invade other tissues or organs.
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Skin Cancer01:30

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Updated: Jan 24, 2026

Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats
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Agreement Among Breast Cancer Screening Modalities in Breast Density Assessment and Cancer Risk Prediction.

Areej S Aloufi1, Salman M Albeshan1, Abdulaziz S Alshabibi1

  • 1Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia, ksu.edu.sa.

International Journal of Breast Cancer
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

Magnetic resonance imaging (MRI) shows promise in predicting breast cancer risk by better identifying dense breast tissue compared to other imaging methods. Further research is needed to standardize breast density assessment for improved screening accuracy.

Keywords:
DBTMRIbreast cancerdense breastmammographyultrasound

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Area of Science:

  • Radiology and Medical Imaging
  • Oncology
  • Biostatistics

Background:

  • Breast density is a significant risk factor for breast cancer detection.
  • Accurate breast density assessment is crucial for effective early detection and risk prediction.

Purpose of the Study:

  • To evaluate agreement across breast density measurements from various imaging modalities.
  • To identify the most effective breast density assessment method for breast cancer prediction.

Main Methods:

  • Compared mammography, synthetic mammography of digital breast tomosynthesis (SM-DBT), ultrasound (US), and MRI for breast density assessment.
  • Utilized American College of Radiology (ACR) categories and visual analog scale (%PD) for density evaluation.
  • Assessed agreement using kappa (k) and Spearman correlation (ρ) coefficients; used logistic regression for risk prediction.

Main Results:

  • Highest agreement for categorical density was between mammography and SM-DBT (k=0.535).
  • MRI demonstrated strong correlations with mammography (ρ=0.56) and SM-DBT (ρ=0.56) for %PD.
  • MRI (OR=4.16) and mammography (OR=3.09) were significant predictors of breast cancer risk.

Conclusions:

  • Significant variability exists in breast density assessment across imaging modalities.
  • MRI shows potential for improved dense breast tissue identification and breast cancer risk prediction.
  • Standardized breast density assessment methods are needed; larger studies are warranted.