Automated Breast Density Measurement in Mammography for Cancer Risk Stratification

Abstract

HomeRadiology: Imaging CancerVol. 3, No. 6 PreviousNext Research HighlightsFree AccessAutomated Breast Density Measurement in Mammography for Cancer Risk StratificationSurrin DeenSurrin DeenSurrin DeenPublished Online:Nov 26 2021https://doi.org/10.1148/rycan.2021219025MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Take-Away Points■ Major Focus: Breast density (BD) was quantified using automated software and compared with subjective radiologist rating of BD.■ Key Result: Automated software improved discrimination of cancer type and of interval cancers compared with visual assessment by a radiologist.■ Impact: Automated BD measurements from mammograms have the potential to stratify patients undergoing breast cancer screening, reducing false-positive rates and the frequency of unnecessary further tests such as MRI or biopsy.Stratified or risk-based breast cancer screening aims to select the most suitable personalized screening regimen for individual patients by estimating the likelihood of developing cancer and balancing this against the negative impact of unnecessary tests. Risk-based stratification algorithms incorporate BD classified subjectively by a radiologist in Breast Imaging Reporting and Data System.In the retrospective case-control study highlighted here, the authors used a U.K.-based population database, the OPTIMAM Database, to identify screening mammograms with cancer in women aged 47EN_DASH73 years. Cancer-free controls (n = 605) were identified and matched to cancer cases (n = 599) on the basis of age and the imaging equipment used. Automated software (Volpara, version 1.5.1; Volpara Health Technologies) calculated fibroglandular volume (FGV), volumetric BD (VBD), and density grade (DG). This was compared to BD assessed by a blinded radiologist using a visual analogue scale (VAS) from 0 to 100.The relative discriminative ability of FGV, both overall and for individual cancer subtypes, was found to be either equivalent to or greater than that of VAS or VBD, whether using statistical analyses based on logistic regression, receiver operating characteristic curves, or number of cancers included in the highest risk category (highest quartile). FGV provided the steepest risk gradient for all cancers, with an odds ratio (OR) for the highest quartile compared to the lowest quartile of 3.7 (95% CI: 2.5, 5.6). FGV quartile, VBD quartile, and DG also predicted node-positive cancers, and FGV quartile demonstrated the steepest risk gradient for interval (OR, 5.3; CI: 3.1, 9.1; P < .01), node-positive (OR, 4.7; CI: 2.5, 9.0; P < .01), and combined cancers (OR, 4.7; CI: 2.9, 7.8; P < .01).This study suggests that automated FGV may provide a good foundation for stratified breast cancer screening. However, information on several covariates was not collected in this study, necessitating further work to confirm this finding.Highlighted ArticleBurnside ES, Warren LM, Myles J, et al. Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study. Br J Cancer 2021;125(6):884–892. doi: https://doi.org/10.1038/s41416-021-01466-yHighlighted ArticleBurnside ES, Warren LM, Myles J, et al. Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study. Br J Cancer 2021;125(6):884–892. doi: https://doi.org/10.1038/s41416-021-01466-y Crossref, Medline, Google ScholarArticle HistoryPublished online: Nov 26 2021 FiguresReferencesRelatedDetailsRecommended Articles Impact of New Technology Adoption on Breast Cancer ScreeningRadiology2018Volume: 290Issue: 3pp. 638-639Age-based versus Risk-based Mammography Screening in Women 40–49 Years Old: A Cross-sectional StudyRadiology2019Volume: 292Issue: 2pp. 321-328MRI Screening Interval in Women with a History of Breast CancerRadiology2021Volume: 300Issue: 2pp. 312-313Fully Automated Volumetric Breast Density Estimation from Digital Breast TomosynthesisRadiology2021Volume: 301Issue: 3pp. 561-568Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 WomenRadiology2021Volume: 301Issue: 3pp. 550-558See More RSNA Education Exhibits Let’s Talk about Next-Generation Breast Cancer Screening Programs: How Should We Do? What Should We Use?Digital Posters2020The Impact of New Genetic Mutations Associated with Breast Cancer on Breast MRI ScreeningDigital Posters2020Dense Breast: Is It Really Looking for a Needle in the Haystack? Digital Posters2020 RSNA Case Collection Malignancy on abbreviated screening breast MRIRSNA Case Collection2020Multifocal breast cancerRSNA Case Collection2020Breast density change with hormone replacement RSNA Case Collection2021 Vol. 3, No. 6 Metrics Altmetric Score PDF download