Mammographic Density Category Research Articles

Comparison of Danish dichotomous and BI-RADS classifications of mammographic density.

BackgroundIn the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before.PurposeTo compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system.Material and MethodsThe study sample consisted of 120 mammograms taken in Copenhagen in 1991–2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic.ResultsOf the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75).ConclusionThe dichotomous mammographic density classification system utilized in early years of Copenhagen’s mammographic screening program (1991–2001) agreed well with the BI-RADS density classification system.

Comparison between Automatic and Semiautomatic Thresholding Method for Mammographic Density Classification

Mammographic density is a novel independent risk factor of breast cancer that reflects the amount of fibroglandular tissue. Breast Imaging Reporting and Data System (BIRADS) density is one of the mammographic density classification schemes which are most widely used by radiologists. Initially, the method used for assessing mammographic density was subjective and qualitative. Recently however, the measurement of mammographic density is more objective and quantitative. In this paper, we propose an alternative model of breast cancer risk factor assessment based on a quantitative approach of density mammogram. This model consists of pre-processing, breast area counting, fibroglandular tissue area counting that uses maximum entropy and multilevel thresholds, and finally breast density counting to determine the risk factor of breast cancer. The proposed model has been tested on a private database from Oncology Clinic Kotabaru, Yogyakarta, Indonesia consisting of 30 mammograms and has been analyzed by some radiologists using the semiautomatic threshold. The result shows that percentage of mammographic density counted by maximum entropy threshold method has the accuracy, sensitivity and specificity of about 87%, 73% and 91% respectively compared to the semiautomatic thresholding method. On the other hand, the accuracy, sensitivity and specificity resulted from using multilevel threshold is about 93%, 87% and 96% respectively. The obtained results suggest that multilevel threshold is perfectly suited for getting quantitative measurement of mammographic density as one of the strongest risk factors for breast cancer.

Breast Density Legislation

HomeRadiologyVol. 266, No. 3 PreviousNext CommunicationsLetters to the EditorBreast Density LegislationFerris M. Hall Ferris M. Hall Author AffiliationsDepartment of Radiology, Beth Israel Deaconess Medical Center, 300 Brookline Ave, Boston, MA 02215e-mail: [email protected]Ferris M. Hall Published Online:Mar 1 2013https://doi.org/10.1148/radiol.12121953MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1 Hooley RJ, Greenberg KL, Stackhouse RM, Geisel JL, Butler RS, Philpotts LE. Screening US in patients with mammographically dense breasts: initial experience with Connecticut Public Act 09-41. Radiology 2012;265(1):59–69. Link, Google Scholar2 Lee CI, Bassett LW, Lehman CD. Breast density legislation and opportunities for patient-centered outcomes research. Radiology 2012;264(3):632–636. Link, Google Scholar3 D’Orsi CJ, Sickles EA. To seek perfection or not? That is the question. Radiology 2012;265(1):9–11. Link, Google Scholar4 Hall FM. The radiology report of the future. Radiology 2009;251(2):313–316. Link, Google Scholar5 Hall FM. The rise and impending decline of screening mammography. Radiology 2008;247(3):597–601. Link, Google Scholar6 Berg WA, Blume JD, Cormack JBet al.. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 2008;299(18):2151–2163. Crossref, Medline, Google Scholar7 Oweyssi N. Breast density legislation from a legal perspective. SBI News. Reston, Va: Society of Breast Imaging, Summer 2012; 6 Google Scholar8 Hall FM. Mammographic density categories [letter]. Radiology 2007;245(1):300–301; author reply 301–302. Link, Google Scholar9 Hall FM. Breast imaging and computer-aided detection. N Engl J Med 2007;356(14):1464–1466. Crossref, Medline, Google ScholarArticle HistoryPublished online: Mar 2013Published in print: Mar 2013 FiguresReferencesRelatedDetailsCited ByCancer Epidemiology, Biomarkers & Prevention, Vol. 30, No. 10Contemporary Clinical Trials, Vol. 56American Journal of Obstetrics and Gynecology, Vol. 212, No. 1Women's Health Issues, Vol. 24, No. 3ISRN Radiology, Vol. 2014Recommended Articles How Radiologists Can Improve Breast Cancer ScreeningRadiology2021Volume: 302Issue: 2pp. 295-297Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-AnalysisRadiology2021Volume: 302Issue: 1pp. 88-104Contrast-enhanced Mammography: State of the ArtRadiology2021Volume: 299Issue: 1pp. 36-48Equalizing the Performance of Radiologists and Physician Extenders in Screening MammographyRadiology2022Volume: 0Issue: 0Digital Mammography Is Similar to Screen-Film Mammography for Women with Personal History of Breast CancerRadiology2021Volume: 300Issue: 2pp. 301-302See More RSNA Education Exhibits Artificial Intelligence in Breast Imaging: Past, Present, and FutureDigital Posters2020An Educational Perspective: Surveillance Following Breast Conservation TherapyDigital Posters2019Breast Health in the Elderly: Implications for ImagingDigital Posters2020 RSNA Case Collection Mucinous carcinoma of the breastRSNA Case Collection2020Hyperplastic Residual Breast TissueRSNA Case Collection2022Breast hemangioma RSNA Case Collection2022 Vol. 266, No. 3 Metrics Downloaded 778 times Altmetric Score PDF download

Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study.

IntroductionIt is not clear whether high mammographic density (MD) is equally associated with all subtypes of breast cancer (BC). We investigated the association between MD and subsequent BC, considering invasiveness, means of detection, pathologic subtype, and the time elapsed since mammographic exploration and BC diagnosis.MethodsBC cases occurring in the population of women who attended screening from 1997 through 2004 in Navarre, a Spanish region with a fully consolidated screening program, were identified via record linkage with the Navarre Cancer Registry (n = 1,172). Information was extracted from the records of their first attendance at screening in that period. For each case, we randomly selected four controls, matched by screening round, year of birth, and place of residence. Cases were classified according to invasiveness (ductal carcinoma in situ (DCIS) versus invasive tumors), pathologic subtype (considering hormonal receptors and HER2), and type of diagnosis (screen-detected versus interval cases). MD was evaluated by a single, experienced radiologist by using a semiquantitative scale. Data on BC risk factors were obtained by the screening program in the corresponding round. The association between MD and tumor subtype was assessed by using conditional logistic regression.ResultsMD was clearly associated with subsequent BC. The odds ratio (OR) for the highest MD category (MD >75%) compared with the reference category (MD <10%) was similar for DCIS (OR = 3.47; 95% CI = 1.46 to 8.27) and invasive tumors (OR = 2.95; 95% CI = 2.01 to 4.35). The excess risk was particularly high for interval cases (OR = 7.72; 95% CI = 4.02 to 14.81) in comparison with screened detected tumors (OR = 2.17; 95% CI = 1.40 to 3.36). Sensitivity analyses excluding interval cases diagnosed in the first year after MD assessment or immediately after an early recall to screening yielded similar results. No differences were seen regarding pathologic subtypes. The excess risk associated with MD persisted for at least 7 to 8 years after mammographic exploration.ConclusionsOur results confirm that MD is an important risk factor for all types of breast cancer. High breast density strongly increases the risk of developing an interval tumor, and this excess risk is not completely explained by a possible masking effect.

Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN)

Measurement of mammographic density (MD), one of the leading risk factors for breast cancer, still relies on subjective assessment. However, the consistency of MD measurement in full-digital mammograms has yet to be evaluated. We studied inter- and intra-rater agreement with respect to estimation of breast density in full-digital mammograms, and tested whether any of the women's characteristics might have some influence on them. After an initial training period, three experienced radiologists estimated MD using Boyd scale in a left breast cranio-caudal mammogram of 1,431 women, recruited at three Spanish screening centres. A subgroup of 50 randomly selected images was read twice to estimate short-term intra-rater agreement. In addition, a reading of 1,428 of the images, performed 2 years before by one rater, was used to estimate long-term intra-rater agreement. Pair-wise weighted kappas with 95% bootstrap confidence intervals were calculated. Dichotomous variables were defined to identify mammograms in which any rater disagreed with other raters or with his/her own assessment, respectively. The association between disagreement and women's characteristics was tested using multivariate mixed logistic models, including centre as a random-effects term, and taking into account repeated measures when required. All quadratic-weighted kappa values for inter- and intra-rater agreement were excellent (higher than 0.80). None of the studied women's features, i.e. body mass index, brassiere size, menopause, nulliparity, lactation or current hormonal therapy, was associated with higher risk of inter- or intra-rater disagreement. However, raters differed significantly more in images that were classified in the higher-density MD categories, and disagreement in intra-rater assessment was also lower in low-density mammograms. The reliability of MD assessment in full-field digital mammograms is comparable to that for original or digitised images. The reassuring lack of association between subjects' MD-related characteristics and agreement suggests that bias from this source is unlikely.

Less mammographic density after nasal versus oral administration of postmenopausal hormone therapy

ABSTRACTObjective Nasal administration gives a more acute but shorter rise in serum hormone levels than oral administration and may therefore have less effect on the fibroglandular tissue in the breasts. We studied the change in mammographic breast density after nasal vs. oral administration of postmenopausal hormone therapy (PHT).Methods We studied participants in a randomized, controlled trial on the impact of nasal vs. oral administration of PHT (combined 17β-estradiol plus norethisterone) for 1 year. Two radiologists classified mammographic density at baseline and after 1 year into four categories. Also, the percentage density was calculated by a computer-based method. The main outcome measure was the difference in the proportion of women with an increase in mammographic density category after 1 year between the nasal and oral groups. Also, the change in the percentage density was calculated.Results The study group comprised 112 healthy postmenopausal women (mean age 56 years), of whom 53 received oral and 59 intranasal PHT. An increase in mammographic density category after 1 year was seen in 20% of the women in the nasal group and in 34% of the oral group. This resulted in a non-significant difference in the proportion of women in whom mammographic breast density had increased by 214% (95% confidence interval (CI) 230% to 2.7%). The mean change in percentage density was 21.2% in the nasal group and + 1.2% in the oral group, yielding a 22.4% differential effect (95% CI 27.3% to 2.5%).Conclusions One year of nasal PHT gave a smaller, although not statistically significant, increase in mammographic density than oral PHT. Remaining issues are the relation between the route of administration of PHT and breast complaints and breast cancer risk.

Accuracy of Assigned BI-RADS Breast Density Category Definitions

Quantitative criteria for the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories have recently been defined as <25% dense for almost entirely fatty, 25%-50% dense for scattered fibroglandular densities, 51%-75% for heterogeneously dense, and >75% dense for the extremely dense category. The purpose of this study is to compare the range of percent mammographic densities with radiologist-assigned BI-RADS mammographic density categories and compare with the recently issued definitions. In this study, 200 consecutive negative analog screening mammograms were assigned BI-RADS mammographic density categories independently by three radiologists blinded to the other readers' density assignment. Quantitative assessment of percent mammographic density was performed using previously validated software. All three readers agreed on BI-RADS mammographic density categories in 98 cases (49%), and two of three readers agreed in all 200 cases. Using two reader's consensus, median mammographic density (range) was 6.0% (0.5%-19.2%) for fatty, 14.8% (1.2%-52.7%) for scattered densities, 51.2% (15.9%-82.2%) for heterogeneously dense, and 78.4% (60.1%-87.9%) for extremely dense breasts. The percent mammographic density ranges for fatty and extremely dense breasts correlated well with BI-RADS definitions, whereas the ranges of densities in the scattered and heterogeneously dense categories were considerably broader. Fatty and extremely dense BI-RADS categories compare relatively well to defined criteria, and therefore may be helpful in breast cancer risk models. Scattered fibroglandular densities and heterogeneously dense categories have broad percent mammographic density ranges and may not function well in breast cancer risk models.

Symmetry of projection in the quantitative analysis of mammographic images.

Mammographic parenchymal patterns are among the strongest indicators of the risk of developing breast cancer. Risk evaluation through breast patterns may have an important role in studies of the aetiology of breast cancer and for monitoring changes in the breast in evaluating potential risk-modifying interventions. Typically, patterns are assessed by an experienced radiologist according to Wolfe grade, or on a coarse quantitative scale according to percent density. Parenchymal characterization methods, to overcome variability of classification by human observer, are under investigation. These include image segmentation using semi-automatic thresholding and automatic classification through textural and density measures. An important practical question relates to the extent to which information about mammographic pattern is carried by any one of the four views obtained in a typical examination. Specifically, variations of right-left breast symmetry and variations between the two standard views of each breast were tested. The mammograms of 30 premenopausal women, comprising 90 images [30 each of the right cranial-caudal (RCC), left cranial-caudal (LCC) and right medial-lateral oblique (RMLO)] were evaluated. Parameters included both subjective (radiologist classification and interactive image thresholding) and objective (fractal and skewness indices) quantitative measurements of parenchymal pattern. For the parameters tested, a high degree of correlations was observed for measurements on the RCC, LCC and RMLO views. Pearson correlation coefficients between 0.86-0.96 were found for the comparisons of quantitative parameters. The strong correlations suggest that, in the study and application of mammographic density classification, representative information is provided in a single view.