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Updated: Jun 11, 2026

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

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

Published on: August 30, 2013

Comparison of Digital Breast Tomosynthesis and Mammography-based Radiomics for Breast Cancer Risk Assessment:

Alex A Nguyen¹, Eric A Cohen², Omid Haji Maghsoudi³

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, Pa.

Radiology. Imaging Cancer

|July 3, 2025

Summary

This summary is machine-generated.

Three-dimensional (3D) digital breast tomosynthesis (DBT) parenchymal analysis improves breast cancer risk assessment over 2D mammography and density measurements. This 3D radiomic approach offers enhanced risk prediction for women.

Keywords:

Breast Mammography Tomosynthesis Volume Analysis

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Area of Science:

Radiology and Medical Imaging
Oncology
Biostatistics

Background:

Breast density is a significant risk factor for breast cancer.
Current 2D mammography and density measurements have limitations in risk assessment.
Digital breast tomosynthesis (DBT) offers 3D imaging capabilities.

Purpose of the Study:

To compare volumetric radiomic parenchymal pattern analysis from 3D DBT images.
To evaluate performance against 2D digital mammography (DM) and 2D DBT sections.
To assess breast cancer risk estimation relative to breast density measurements.

Main Methods:

Retrospective matched case-control study of concurrent DM and DBT screening.
Radiomic features calculated using the Cancer Phenomics Toolkit from 3D DBT and 2D DM images.
Conditional logistic regression used to associate radiomic features, age, BMI, and area percent density (PD) with breast cancer risk; C statistic used for predictive ability.

Main Results:

Volumetric features from 3D DBT scans showed higher C statistics (mean 0.68) compared to 2D image types (mean 0.60-0.65).
A baseline model with age, BMI, and area PD had a C statistic of 0.60.
Image resolution and window size had minimal impact on performance, suggesting less computationally intensive processing is viable.

Conclusions:

Fully automated 3D parenchymal analysis from DBT significantly improved breast cancer risk estimation.
The 3D radiomic approach surpasses risk markers derived from area breast density and 2D images.
This technique holds promise for more accurate breast cancer risk assessment.