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Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

ABSTRACT: Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted ? statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with ? values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.

SUBMITTER: Brandt KR

PROVIDER: S-EPMC4886704 | biostudies-other | 2016 Jun

REPOSITORIES: biostudies-other

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Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Brandt Kathleen R KR Scott Christopher G CG Ma Lin L Mahmoudzadeh Amir P AP Jensen Matthew R MR Whaley Dana H DH Wu Fang Fang FF Malkov Serghei S Hruska Carrie B CB Norman Aaron D AD Heine John J Shepherd John J Pankratz V Shane VS Kerlikowske Karla K Vachon Celine M CM

Radiology 20151222 3

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date ...[more]

PMID: 26694052

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Project description:Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.

Project description:INTRODUCTION:While Cumulus - a semi-automated method for measuring breast density - is utilised extensively in research, it is labour-intensive and unsuitable for screening programmes that require an efficient and valid measure on which to base screening recommendations. We develop an automated method to measure breast density (AutoDensity) and compare it to Cumulus in terms of association with breast cancer risk and breast cancer screening outcomes. METHODS:AutoDensity automatically identifies the breast area in the mammogram and classifies breast density in a similar way to Cumulus, through a fast, stand-alone Windows or Linux program. Our sample comprised 985 women with screen-detected cancers, 367 women with interval cancers and 4,975 controls (women who did not have cancer), sampled from first and subsequent screening rounds of a film mammography screening programme. To test the validity of AutoDensity, we compared the effect estimates using AutoDensity with those using Cumulus from logistic regression models that tested the association between breast density and breast cancer risk, risk of small and large screen-detected cancers and interval cancers, and screening programme sensitivity (the proportion of cancers that are screen-detected). As a secondary analysis, we report on correlation between AutoDensity and Cumulus measures. RESULTS:AutoDensity performed similarly to Cumulus in all associations tested. For example, using AutoDensity, the odds ratios for women in the highest decile of breast density compared to women in the lowest quintile for invasive breast cancer, interval cancers, large and small screen-detected cancers were 3.2 (95% CI 2.5 to 4.1), 4.7 (95% CI 3.0 to 7.4), 6.4 (95% CI 3.7 to 11.1) and 2.2 (95% CI 1.6 to 3.0) respectively. For Cumulus the corresponding odds ratios were: 2.4 (95% CI 1.9 to 3.1), 4.1 (95% CI 2.6 to 6.3), 6.6 (95% CI 3.7 to 11.7) and 1.3 (95% CI 0.9 to 1.8). Correlation between Cumulus and AutoDensity measures was 0.63 (P < 0.001). CONCLUSIONS:Based on the similarity of the effect estimates for AutoDensity and Cumulus inmodels of breast density and breast cancer risk and screening outcomes, we conclude that AutoDensity is a valid automated method for measuring breast density from digitised film mammograms.

Project description:BackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.MethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts.ResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10- 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10- 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10- 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS.ConclusionsWe have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

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Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

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Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

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