ArticleMedical physics2013
Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography.
Article in Medical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Immediate clip migration after breast biopsy: a meta-analysis for potential risk factors.The British journal of radiology · 2022Pooled it
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- Radiolabeled HER2-targeted molecular probes in breast cancer imaging: current knowledge and future prospective.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
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- A background parenchymal enhancement quantification framework of breast magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2023Article
- Breast Density Analysis Using Digital Breast Tomosynthesis.bioRxiv : the preprint server for biology · 2023Article
- Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.AJR. American journal of roentgenology · 2021Article
- Empirically-derived synthetic populations to mitigate small sample sizes.Journal of biomedical informatics · 2020Article
- Calibrated Breast Density Measurements.Academic radiology · 2019Article
- Use of ultrasound combined with magnetic resonance imaging for diagnosis of breast masses and fibroids.The Journal of international medical research · 2019Article
- Technical challenges in generalizing calibration techniques for breast density measurements.Medical physics · 2019Article
- Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.Academic radiology · 2014Article
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4 authors at 1 institution in 1 country.
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Abstract
purposeThe Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.
methodsA matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.
resultsThe three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.
conclusionsA general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.
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