ArticleJournal of personalized medicine2023
Breast Density Evaluation According to BI-RADS 5th Edition on Digital Breast Tomosynthesis: AI Automated Assessment Versus Human Visual Assessment.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- ROI-Based Quantitative Analysis in Contrast-Enhanced Mammography: A Feasibility Study for Reproducible Enhancement Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Impact of neoadjuvant chemotherapy on breast tissue density and its correlation with pathologic response: a retrospective artificial intelligence-enhanced radiological study.The British journal of radiology · 2026Article
- Latest advances in nanodrug delivery systems for modulating the immune microenvironment in triple-negative breast cancer.Frontiers in immunology · 2026Review
- Enhanced value of chest computed tomography radiomics features in breast density classification.Scientific reports · 2025Article
- Impact of Digital Breast Tomosynthesis on Extra Views and Concomitant Ultrasound Examinations in One Year Post Breast-Conserving Surgery Patients.Journal of medical radiation sciences · 2025Article
- Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks.Insights into imaging · 2025Article
- Assessing artificial intelligence in breast screening with stratified results on 306 839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study.BMJ health & care informatics · 2025Article
- Artificial intelligence and consistency in patient care: a large-scale longitudinal study of mammographic density assessment.BJR artificial intelligence · 2025Article
- A Comparative Analysis of Mobile versus Stationary Mammography Units: Performance and Outcomes in a Breast Cancer Screening Program.Breast cancer (Dove Medical Press) · 2025Article
- Contrast-enhanced mammography in high-dense breasts: a narrative review.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2025Review
- Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.JCO clinical cancer informatics · 2024Article
- Comparison of Visual and Quantra Software Mammographic Density Assessment According to BI-RADSJournal of imaging · 2024Article
- Inter- and intra-observer variability of qualitative visual breast-composition assessment in mammography among Japanese physicians: a first multi-institutional observer performance study in Japan.Breast cancer (Tokyo, Japan) · 2024Article
- Practical Challenges of DBT-Guided VABB: Harms and Benefits, from Literature to Clinical Experience.Cancers · 2023Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe assessment of breast density is one of the main goals of radiologists because the masking effect of dense fibroglandular tissue may affect the mammographic identification of lesions. The BI-RADS 5th Edition has revised the mammographic breast density categories, focusing on a qualitative evaluation rather than a quantitative one. Our purpose is to compare the concordance of the automatic classification of breast density with the visual assessment according to the latest available classification.
methodsA sample of 1075 digital breast tomosynthesis images from women aged between 40 and 86 years (58 ± 7.1) was retrospectively analyzed by three independent readers according to the BI-RADS 5th Edition. Automated breast density assessment was performed on digital breast tomosynthesis images with the Quantra software version 2.2.3. Interobserver agreement was assessed with kappa statistics. The distributions of breast density categories were compared and correlated with age.
resultsThe agreement on breast density categories was substantial to almost perfect between radiologists (κ = 0.63-0.83), moderate to substantial between radiologists and the Quantra software (κ = 0.44-0.78), and the consensus of radiologists and the Quantra software (κ = 0.60-0.77). Comparing the assessment for dense and non-dense breasts, the agreement was almost perfect in the screening age range without a statistically significant difference between concordant and discordant cases when compared by age.
conclusionsThe categorization proposed by the Quantra software has shown a good agreement with the radiological evaluations, even though it did not completely reflect the visual assessment. Thus, clinical decisions regarding supplemental screening should be based on the radiologist's perceived masking effect rather than the data produced exclusively by the Quantra software.
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