Automated Measurement of Volumetric Mammographic Density: A Tool for Widespread Breast Cancer Risk Assessment

Brand, Judith S.; Czene, Kamila; Shepherd, John; Leifland, Karin; Heddson, Boel; Sundbom, Ann; Eriksson, Mikael; Li, Jingmei; Humphreys, Keith; Hall, Per

doi:10.1158/1055-9965.epi-13-1219

Cited by 64 publications

(54 citation statements)

References 38 publications

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“…Park et al evaluated VPD with Volpara as a risk factor among Korean women (n = 677 cases) and found associations of similar magnitude to those in our study among postmenopausal women (22). Brand et al (28) found VPD and DV, estimated with Volpara on mammograms a median of 22 days from cancer diagnosis, to be associated with breast cancer in a cohort study, with 206 incident cases (28). The magnitude of risk between the highest and lowest quartiles of VPD in the lower but positive risk estimates when using quintiles of density (OR, 2.6 with Volpara and 2.0 with Quantra).…”

Section: Breast Imaging: Comparison Of Clinical and Automated Breast supporting

confidence: 86%

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

Brandt¹,

Scott²,

Ma³

et al. 2016

Radiology

160

108

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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 andMethods: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 k 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 k 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.q RSNA, 2015

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Section: Breast Imaging: Comparison Of Clinical and Automated Breast supporting

confidence: 86%

“…There are several limitations of this study, including the fact that we study of Brand et al was similar to ours (OR of 2.9 and 2.8, respectively) (28).…”

Section: Breast Imaging: Comparison Of Clinical and Automated Breast mentioning

confidence: 45%

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

Brandt¹,

Scott²,

Ma³

et al. 2016

Radiology

160

108

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“…However, Cumulus suffers from the same shortcomings as BI-RADS. Use of mammographic density in future risk prediction models will benefit from fully automated programs for measurement of mammographic density, such as the Volpara program (40,41). Compared with BI-RADS, advantages of automated systems include objectivity in measures and a continuous scale from which the entire spectrum of density values can provide information.…”

Section: Prevention Trials and Clinical Programsmentioning

confidence: 99%

Towards Prevention of Breast Cancer: What Are the Clinical Challenges?

Borgquist

Hall

Lipkus

et al. 2018

Cancer Prevention Research

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The dramatic increase in breast cancer incidence compels a paradigm shift in our preventive efforts. There are several barriers to overcome before prevention becomes an established part of breast cancer management. The objective of this review is to identify the clinical challenges for improved breast cancer prevention and discuss current knowledge on breast cancer risk assessment methods, risk communication, ethics, and interventional efforts with the aim of covering the aspects relevant for a breast cancer prevention trial. Herein, the following five areas are discussed: (i) Adequate tools for identification of women at high risk of breast cancer suggestively entitled Prevent! Breast cancer is the most common cancer in women worldwide with a steady increase in incidence, particularly in the developing world (1-3). An expanding middle class and a more westernized lifestyle contribute to increasing breast cancer rates in less developed countries as well (4-7), while limited health care resources and insufficient infrastructure challenge the management of the rising number of breast cancer patients (8). The dramatic increase in breast cancer incidence compels a paradigm shift in our preventive efforts. Despite the increasing incidence, few preventive measures have been implemented apart from identification and surveillance of genetically identified high-risk women with BRCA1 and BRCA2 mutations (9). Early detection through mammography screening and treatment of newly diagnosed cancers represent current clinical standards. There are several barriers to overcome before prevention becomes an established part of breast cancer management (Table 1). Consequently, the success of prevention efforts relies strongly on an ambitious interdisciplinary approach dedicated to the potential high-yield gain of intercepting breast cancer development (10).The initiative to this review originated while preparing a breast cancer prevention trial and identifying the gap of comprehensively bridging, not only risk assessment and intervention, but also communication of risk. Thus, the objective of this review is to identify the clinical challenges for improved breast cancer prevention and discuss current knowledge on breast cancer risk assessment methods, risk communication, ethics, and interventional efforts with the aim of covering aspects relevant for a prevention trial. Future comprehensive risk prediction models and effective communication hereof hopefully will allow for state-ofthe-art identification of high-risk women among whom individualized surveillance and prevention through adequate intervention can reduce breast cancer incidence. Breast Cancer Risk Prediction ModelsCurrent breast cancer risk assessment tools and prediction modelsThe identification of women at increased risk for developing breast cancer improves the likelihood these women will benefit from intensified surveillance and/or preventive intervention. Risk assessment systems have been developed successfully for women with a strong family history of breast c...

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“…These algorithms can be highly nonlinear and are highly proprietary. The effect of this on MD was assessed recently by Brand et al 17 They conducted a comparative study using Volpara on five different mammographic imaging systems assessing breast cancer risk with automated measurement. The authors reported that with the exclusion of the small GE detector, minor differences were noted between GE, Phillips, and Sectra systems, with the largest relative difference in percent dense volume being 3%.…”

Section: Introductionmentioning

confidence: 99%

Mammographic density measurements are not affected by mammography system

Damases¹,

Brennan

McEntee

2015

J. Med. Imag

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Abstract. Mammographic density (MD) is a significant risk factor for breast cancer and has been shown to reduce the sensitivity of mammography screening. Knowledge of a woman's density can be used to predict her risk of developing breast cancer and personalize her imaging pathway. However, measurement of breast density has proven to be troublesome with wide variations in density recorded using radiologists' visual Breast Imaging Reporting and Data System (BIRADS). Several automated methods for assessing breast density have been proposed, each with their own source of measurement error. The use of differing mammographic imaging systems further complicates MD measurement, especially for the same women imaged over time. The purpose of this study was to investigate whether having a mammogram on differing manufacturer's equipment affects a woman's MD measurement. Raw mammographic images were acquired on two mammography imaging systems (General Electric and Hologic) one year apart and processed using VolparaDensity™ to obtain the Volpara Density Grade (VDG) and average volumetric breast density percentage (AvBD%). Visual BIRADS scores were also obtained from 20 expert readers. BIRADS scores for both systems showed strong positive correlation (ρ ¼ 0.904; p < 0.001), while the VDG (ρ ¼ 0.978; p < 0.001) and AvBD% (ρ ¼ 0.973; p < 0.001) showed stronger positive correlations. Substantial agreement was shown between the systems for BIRADS (κ ¼ 0.692; p < 0.001), however, the systems demonstrated an almost perfect agreement for VDG (κ ¼ 0.933; p < 0.001).

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Automated Measurement of Volumetric Mammographic Density: A Tool for Widespread Breast Cancer Risk Assessment

Cited by 64 publications

References 38 publications

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

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

Towards Prevention of Breast Cancer: What Are the Clinical Challenges?

Mammographic density measurements are not affected by mammography system

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