ArticleBreast cancer research : BCR2019
Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark.
Article in Breast cancer research : BCR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05006196 (Contrast-Free Magnetic Resonance Imaging for Breast Disease), which is not on this map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Contrast-Free Magnetic Resonance Imaging for Breast Disease
Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.
- A Quantitative Framework to Study Potential Benefits and Harms of Multi-Cancer Early Detection Testing.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Pooled it
- A prospective trial for breast cancer diagnosis by canine odorology.Nature communications · 2025Trial
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- Effect of an annotation-free artificial intelligence system for simultaneous detection and diagnosis on breast ultrasonography: a multireader, multicase study across diverse healthcare professionals.Ultrasonography (Seoul, Korea) · 2026Article
- Portable Freehand 3D Breast Ultrasound Using a Dual-Rotary-Encoder 2DoF Tracking Framework.Sensors (Basel, Switzerland) · 2026Article
- Imaging of Artificial Tumor Models in an Anatomical Breast Phantom with a Single-Sided Magnetic Particle Imaging Scanner.Tomography (Ann Arbor, Mich.) · 2026Article
- Development and Validation of a Machine-Learning Deep Plasma Proteome Classifier for Early-Stage Breast Cancer Detection.Breast cancer (Dove Medical Press) · 2026Article
- Multimodal deep learning for breast tumor classification: Integrating mammography and ultrasound for enhanced diagnostic accuracy.Journal of applied clinical medical physics · 2026Article
- Polychlorinated biphenyls and breast cancer: evidence, mechanisms, and risk management.Frontiers in cell and developmental biology · 2026Review
- Modelling image quality over extended [Frontiers in oncology · 2026Article
- Screening Outcomes of Supplemental Automated Breast Ultrasound in Women With Nondense Breasts Undergoing Mammography.Korean journal of radiology · 2026Article
- Exon-Specific Targeted Analysis ofCancer informatics · 2026Article
- Dual-modality CAD for breast cancer screening: dealing with discordant diagnosis between mammography and tomography.Frontiers in oncology · 2026Article
- Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization.Nature biomedical engineering · 2026Article
- Development and validation of an integrated model combining deep learning, radiomics, and clinical and breast ultrasound features for Breast Imaging Reporting and Data System 4A lesion malignancy classification.Quantitative imaging in medicine and surgery · 2025Article
- Clinical application of gadoterate meglumine in abbreviated dynamic contrast-enhanced magnetic resonance imaging of the breast with ultrafast imaging: a single-center retrospective study.Quantitative imaging in medicine and surgery · 2025Article
- Exploring the Efficacy of Artificial Intelligence-Based Computer-Aided Detection for Breast Cancer Detection on Digital Mammograms.Journal of the Korean Society of Radiology · 2025Article
- Exploring the Evolution of Breast Cancer Imaging: A Review of Conventional and Emerging Modalities.Cureus · 2025Review
- Subgroups varying in risk and density highlight the potential for stratified breast cancer screening.European journal of cancer (Oxford, England : 1990) · 2025Article
- Transforming cancer screening: the potential of multi-cancer early detection (MCED) technologies.International journal of clinical oncology · 2025Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundScreening mammography works better in fatty than in dense breast tissue. Computerized assessment of parenchymal texture is a non-subjective method to obtain a refined description of breast tissue, potentially valuable in addition to breast density scoring for the identification of women in need of supplementary imaging. We studied the sensitivity of screening mammography by a combination of radiologist-assessed Breast Imaging Reporting and Data System (BI-RADS) density score and computer-assessed parenchymal texture marker, mammography texture resemblance (MTR), in a population-based screening program.
methodsBreast density was coded according to the fourth edition of the BI-RADS density code, and MTR marker was divided into quartiles from 1 to 4. Screening data were followed up for the identification of screen-detected and interval cancers. We calculated sensitivity and specificity with 95% confidence intervals (CI) by BI-RADS density score, MTR marker, and combination hereof.
resultsDensity and texture were strongly correlated, but the combination led to the identification of subgroups with different sensitivity. Sensitivity was high, about 80%, in women with BI-RADS density score 1 and MTR markers 1 or 2. Sensitivity was low, 67%, in women with BI-RADS density score 2 and MTR marker 4. For women with BI-RADS density scores 3 and 4, the already low sensitivity was further decreased for women with MTR marker 4. Specificity was 97-99% in all subgroups.
conclusionOur study showed that women with low density constituted a heterogenous group. Classifying women for extra imaging based on density only might be a too crude approach. Screening sensitivity was systematically high in women with fatty and homogenous breast tissue.
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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.