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

Kathleen R Brandt, Christopher G. Scott, Lin Ma, Amir P. Mahmoudzadeh, Matthew R. Jensen, Dana H. Whaley, Fang Fang Wu, Serghei Malkov, Carrie B Hruska, Aaron D. Norman, John Heine, John Shepherd, V. Shane Pankratz, Karla Kerlikowske, Celine M Vachon

Research output: Contribution to journalArticle

65 Citations (Scopus)

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 κ 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 κ 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. (

Original languageEnglish (US)
Pages (from-to)710-719
Number of pages10
JournalRadiology
Volume279
Issue number3
DOIs
StatePublished - Jun 1 2016

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Breast
Information Systems
Confidence Intervals
Breast Neoplasms
Mammography
Breast Density
Odds Ratio
Physicians' Practice Patterns
Health Insurance Portability and Accountability Act
San Francisco
Research Ethics Committees
Informed Consent
New Zealand
Registries
Body Mass Index
Logistic Models
Technology
Research

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Comparison of Clinical and Automated Breast Density Measurements : Implications for Risk Prediction and Supplemental Screening. / Brandt, Kathleen R; Scott, Christopher G.; Ma, Lin; Mahmoudzadeh, Amir P.; Jensen, Matthew R.; Whaley, Dana H.; Wu, Fang Fang; Malkov, Serghei; Hruska, Carrie B; Norman, Aaron D.; Heine, John; Shepherd, John; Pankratz, V. Shane; Kerlikowske, Karla; Vachon, Celine M.

In: Radiology, Vol. 279, No. 3, 01.06.2016, p. 710-719.

Research output: Contribution to journalArticle

Brandt, KR, Scott, CG, Ma, L, Mahmoudzadeh, AP, Jensen, MR, Whaley, DH, Wu, FF, Malkov, S, Hruska, CB, Norman, AD, Heine, J, Shepherd, J, Pankratz, VS, Kerlikowske, K & Vachon, CM 2016, 'Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening', Radiology, vol. 279, no. 3, pp. 710-719. https://doi.org/10.1148/radiol.2015151261
Brandt, Kathleen R ; Scott, Christopher G. ; Ma, Lin ; Mahmoudzadeh, Amir P. ; Jensen, Matthew R. ; Whaley, Dana H. ; Wu, Fang Fang ; Malkov, Serghei ; Hruska, Carrie B ; Norman, Aaron D. ; Heine, John ; Shepherd, John ; Pankratz, V. Shane ; Kerlikowske, Karla ; Vachon, Celine M. / Comparison of Clinical and Automated Breast Density Measurements : Implications for Risk Prediction and Supplemental Screening. In: Radiology. 2016 ; Vol. 279, No. 3. pp. 710-719.
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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 κ 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 κ 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. (",
author = "Brandt, {Kathleen R} and Scott, {Christopher G.} and Lin Ma and Mahmoudzadeh, {Amir P.} and Jensen, {Matthew R.} and Whaley, {Dana H.} and Wu, {Fang Fang} and Serghei Malkov and Hruska, {Carrie B} and Norman, {Aaron D.} and John Heine and John Shepherd and Pankratz, {V. Shane} and Karla Kerlikowske and Vachon, {Celine M}",
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language = "English (US)",
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TY - JOUR

T1 - Comparison of Clinical and Automated Breast Density Measurements

T2 - Implications for Risk Prediction and Supplemental Screening

AU - Brandt, Kathleen R

AU - Scott, Christopher G.

AU - Ma, Lin

AU - Mahmoudzadeh, Amir P.

AU - Jensen, Matthew R.

AU - Whaley, Dana H.

AU - Wu, Fang Fang

AU - Malkov, Serghei

AU - Hruska, Carrie B

AU - Norman, Aaron D.

AU - Heine, John

AU - Shepherd, John

AU - Pankratz, V. Shane

AU - Kerlikowske, Karla

AU - Vachon, Celine M

PY - 2016/6/1

Y1 - 2016/6/1

N2 - 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 κ 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 κ 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. (

AB - 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 κ 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 κ 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. (

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