ArticleBreast cancer research and treatment2017
Quantification of masking risk in screening mammography with volumetric breast density maps.
Article in Breast cancer research and treatment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- More than density: validating a mammographic masking prediction model in Dutch breast cancer screening.European radiology · 2025Article
- Assessing the generalisation of artificial intelligence across mammography manufacturers.PLOS digital health · 2025Article
- Breast Cancer Screening in Asian Countries: Epidemiology, Screening Practices, Outcomes, Challenges, and Future Directions.Korean journal of radiology · 2025Review
- Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.Scientific reports · 2025Article
- Deep learning prediction of mammographic breast density using screening data.Scientific reports · 2025Article
- AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.Journal of imaging · 2024Article
- Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.Journal of digital imaging · 2023Article
- Validity of computed mean compressed fibroglandular tissue thickness and breast composition for stratification of masking risk in Japanese women.Breast cancer (Tokyo, Japan) · 2023Article
- Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.Radiology · 2023Review
- Understanding mammographic breast density profile in China: A Sino-Australian comparative study of breast density using real-world data from cancer screening programs.Asia-Pacific journal of clinical oncology · 2022Article
- Decision quality and regret with treatment decisions in women with breast cancer: Pre-operative breast MRI and breast density.Breast cancer research and treatment · 2022Article
- Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning.Scientific reports · 2022Article
- Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.Scientific reports · 2021Article
- Double reading in breast cancer screening: considerations for policy-making.The British journal of radiology · 2020Review
- Investigating the feasibility of stratified breast cancer screening using a masking risk predictor.Breast cancer research : BCR · 2019Article
- Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis.Medical physics · 2019Article
- Localized mammographic density is associated with interval cancer and large breast cancer: a nested case-control study.Breast cancer research : BCR · 2019Article
- Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement.Journal of oncology · 2019Article
- Prediction of glandularity and breast radiation dose from mammography results in Japanese women.Medical & biological engineering & computing · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFibroglandular tissue may mask breast cancers, thereby reducing the sensitivity of mammography. Here, we investigate methods for identification of women at high risk of a masked tumor, who could benefit from additional imaging.
methodsThe last negative screening mammograms of 111 women with interval cancer (IC) within 12 months after the examination and 1110 selected normal screening exams from women without cancer were used. From the mammograms, volumetric breast density maps were computed, which provide the dense tissue thickness for each pixel location. With these maps, three measurements were derived: (1) percent dense volume (PDV), (2) percent area where dense tissue thickness exceeds 1 cm (PDA), and (3) dense tissue masking model (DTMM). Breast density was scored by a breast radiologist using BI-RADS. Women with heterogeneously and extremely dense breasts were considered at high masking risk. For each masking measure, mammograms were divided into a high- and low-risk category such that the same proportion of the controls is at high masking risk as with BI-RADS.
resultsOf the women with IC, 66.1, 71.9, 69.2, and 63.0% were categorized to be at high masking risk with PDV, PDA, DTMM, and BI-RADS, respectively, against 38.5% of the controls. The proportion of IC at high masking risk is statistically significantly different between BI-RADS and PDA (p-value 0.022). Differences between BI-RADS and PDV, or BI-RADS and DTMM, are not statistically significant.
conclusionMeasures based on density maps, and in particular PDA, are promising tools to identify women at high risk for a masked cancer.
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