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. Pankratz Shane, Karla Kerlikowske, Celine M. Vachon

Research output: Contribution to journalArticle

76 Scopus citations

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 BIRADS 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.

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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Brandt, K. R., Scott, C. G., Ma, L., Mahmoudzadeh, A. P., Jensen, M. R., Whaley, D. H., Wu, F. F., Malkov, S., Hruska, C. B., Norman, A. D., Heine, J., Shepherd, J., Shane, V. P., Kerlikowske, K., & Vachon, C. M. (2016). Measurements: Implications for risk prediction and supplemental screening. Radiology, 279(3), 710-719. https://doi.org/10.1148/radiol.2015151261