ArticleRadiology. Imaging cancer2025
Comparison of Digital Breast Tomosynthesis and Mammography-based Radiomics for Breast Cancer Risk Assessment: Case-Control Study.
Article in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Microwave breast imaging: A review of clinical potential and technological advances.World journal of methodology · 2026Review
- Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
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Authors and funding
13 authors.
Funding
Abstract
Purpose To compare the performance of volumetric radiomic parenchymal pattern analysis from three-dimensional (3D) digital breast tomosynthesis (DBT) images with that of two-dimensional (2D) digital mammography (DM) and 2D sections from DBT in assessing breast cancer risk relative to breast density measurements. Materials and Methods This was a retrospective matched case-control study among individuals who underwent concurrent DM and DBT screening from March 2011 through December 2014. The Cancer Phenomics Toolkit was used to calculate radiomic features from craniocaudal and mediolateral oblique views in all study patients, matched on race and age, for various experimental settings, including image resolution and window size. For each image type, conditional logistic regression evaluated the association of radiomic features, along with age, body mass index (BMI), and area percent density (PD) (from the Laboratory for Individualized Breast Radiodensity Assessment software), with breast cancer, using the C statistic as the measure of model predictive ability. Model fit was compared via likelihood ratio tests. Results The study included 924 female patients (median age, 61 years [IQR: 51-69 years]), with 187 cases and 737 controls. Volumetric features from 3D reconstructed DBT scans had, on average, higher C statistics across all experimental conditions. Among models using only radiomic features, C statistics were highest for models using features from 3D images (mean C statistic: 0.68,
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