ArticleMedical image analysis2021
Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 65 citations in OpenAlex.
- Enhancing Breast Density Assessment in Mammograms Through Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Review
- Advancements in Developing an Automated Breast Density Detection Technique for Breast Cancer Risk Prediction: Synthesizing a Signal-dependent Noise Stochastic Process.bioRxiv : the preprint server for biology · 2026Article
- Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications.Journal of medical radiation sciences · 2026Article
- Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Development and evaluation of artificial intelligence tools to estimate volumetric breast density from processed 2D mammograms.BJR artificial intelligence · 2026Article
- Comparing percent breast density assessments of an AI-based method with expert reader estimates: inter-observer variability.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Breast density in MRI: an AI-based quantification and relationship to assessment in mammography.NPJ breast cancer · 2025Article
- Evaluating mammographic density's contribution to improve a breast cancer risk model with questionnaire-based and polygenic factors.NPJ breast cancer · 2025Article
- Maximum-likelihood estimation of glandular fraction for mammography and its effect on microcalcification detection.Physical and engineering sciences in medicine · 2025Article
- Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.IEEE journal of biomedical and health informatics · 2025Article
- Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.Scientific reports · 2025Article
- Concordant and discordant breast density patterns by different approaches for assessing breast density and breast cancer risk.Breast cancer research and treatment · 2025Article
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Uncertainty-Aware Semi-Supervised Method for Pectoral Muscle Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- CLGB-Net: fusion network for identifying local and global information of lesions in digital mammography images.Frontiers in oncology · 2025Article
- Investigating the added value of incorporatingmammographic density to an integrated breastcancer risk model with questionnaire-based riskfactors and polygenic risk score.Research square · 2024Article
- Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.JCO clinical cancer informatics · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.