Top

Breast Cancer

Published in:

01-05-2019 | Original Article

Can quantitative evaluation of mammographic breast density, “volumetric measurement”, predict the masking risk with dense breast tissue? Investigation by comparison with subjective visual estimation by Japanese radiologists

Authors: Mikinao Oiwa, Tokiko Endo, Namiko Suda, Takako Morita, Yasuyuki Sato, Tomonori Kawasaki, Shu Ichihara

Published in: Breast Cancer | Issue 3/2019

Abstract

Background

Sensitivity to detect breast cancer (BC) is not high in a dense breast due to masking in mammography. To evaluate the breast density, a volumetric measurement system has been recently developed that measures the percent fibroglandular tissue volume (percent FGV, hereafter termed as “FG%”) to the breast volume (BV). This study was designed to investigate whether evaluation using FG% can accurately predict the masking risk by comparing with the current standard method of subjective visual estimation (SVE).

Methods

Using pre-biopsy mammograms of 114 cases histopathologically diagnosed with BC in our facility, SVE based on BI-RADS (5th edition) and volumetric measurements of FG% were conducted. Performance to predict the masking risk was evaluated using the area under the receiver operating characteristic curve (AUC). Relationship between these parameters and the masking risk was evaluated by the adjusted multivariate linear regression analysis.

Results

The AUC of SVE values was 0.742 (95% CI 0.641–0.822), while that of FG% was as significantly low as 0.560 (95% CI 0.427–0.685) (P = 0.0014). The SVE values correlated with the detection of BC in mammography (P = 0.0035), but there was no significant relationship with FG% (P = 0.74). The median BV and FGV were 313 cm³ (IQR 191–440) and 63 cm³ (IQR 44–102), respectively. The FGV was comparable to the data for Caucasian women reported in previous studies, but the BV was one-half of the previous data.

Conclusion

The current volumetric measurement system to evaluate FG% to BV was found to be insufficient in the performance to predict the masking risk in Japanese women with relatively small-sized breasts.

Saquib N, Saquib J, Ioannidis JP. Does screening for disease save lives in asymptomatic adults? Systematic review of meta-analyses and randomized trials. Int J Epidemiol. 2015;44:264–77.CrossRefPubMed

Mandelson MT, Oestreicher N, Porter PL, White D, Finder CA, Taplin SH, et al. Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst. 2000;92:1081–7.CrossRefPubMed

Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002;225:165–75.CrossRef

Carney PA, Miglioretti DL, Yankaskas BC, Kerlikowske K, Rosenberg R, Rutter CM, et al. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med. 2003;138:168–75.CrossRef

Kerlikowske K. The mammogram that cried Wolfe. N Engl J Med. 2007;356:297–300.CrossRefPubMed

Harvey JA, Bovbjerg VE. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology. 2004;230:29–41.CrossRefPubMed

Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356:227–36.CrossRefPubMed

Razzaghi H, Troester MA, Gierach GL, Olshan AF, Yankaskas BC, Millikan RC. Mammographic density and breast cancer risk in White and African American Women. Breast Cancer Res Treat. 2012;135:571–80.CrossRefPubMedPubMedCentral

Maskarinec G, Nagata C, Shimizu H, Kashiki Y. Comparison of mammographic densities and their determinants in women from Japan and Hawaii. Int J Cancer. 2002;102:29–33.CrossRefPubMed

10.

Habel LA, Capra AM, Oestreicher N, Greendale GA, Cauley JA, Bromberger J, et al. Mammographic density in a multiethnic cohort. Menopause. 2007;14:891–9.CrossRefPubMed

11.

Heller SL, Hudson S, Wilkinson LS. Breast density across a regional screening population: effects of age, ethnicity and deprivation. Br J Radiol. 2015;88:20150242.CrossRefPubMedPubMedCentral

12.

Dai H, Yan Y, Wang P, Liu P, Cao Y, Xiong L, et al. Distribution of mammographic density and its influential factors among Chinese women. Int J Epidemiol. 2014;43:1240–51.CrossRefPubMedPubMedCentral

13.

Japanese Association of Breast Cancer Screening, Japanese Breast Cancer Society, the Japan Central Organization on Quality Assurance of Breast Cancer Screening. Recommendations for the problem of “dense breast” in population-based breast cancer screening. http://www.jabcs.jp/pdf/DBWGreport.pdf (in Japanese). 2018. Accessed 7 Apr 2018.

14.

Eng A, Gallant Z, Shepherd J, McCormack V, Li J, Dowsett M, et al. Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res. 2014;16:439.CrossRefPubMedPubMedCentral

15.

Brand JS, Czene K, Shepherd JA, Leifland K, Heddson B, Sundbom A, et al. Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment. Cancer Epidemiol Biomark Prev. 2014;23:1764–72.CrossRef

16.

Brandt KR, Scott CG, Ma L, Mahmoudzadeh AP, Jensen MR, Whaley DH, et al. Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening. Radiology. 2016;279:710–9.CrossRefPubMed

17.

Park IH, Ko K, Joo J, Park B, Jung SY, Lee S, et al. High volumetric breast density predicts risk for breast cancer in postmenopausal, but not premenopausal, Korean Women. Ann Surg Oncol. 2014;21:4124–32.CrossRefPubMed

18.

Sickles EA, D’Orsi CJ, Bassett LW, Appleton CM, Berg WA, Burnside ES, et al. Breast imaging reporting and data system (BI-RADS) atlas. 5th ed. Reston, VA: American College of Radiology; 2013.

19.

Berg WA, Campassi C, Langenberg P, Sexton MJ. Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. AJR Am J Roentgenol. 2000;174:1769–77.CrossRefPubMed

20.

Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, et al. Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. Breast. 2005;14:269–75.CrossRefPubMed

21.

Redondo A, Comas M, Macia F, Ferrer F, Murta-Nascimento C, Maristany MT, et al. Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. Br J Radiol. 2012;85:1465–70.CrossRefPubMedPubMedCentral

22.

Wang J, Azziz A, Fan B, Malkov S, Klifa C, Newitt D, et al. Agreement of mammographic measures of volumetric breast density to MRI. PLoS One. 2013;8:e81653.CrossRefPubMedPubMedCentral

23.

Gubern-Merida A, Kallenberg M, Platel B, Mann RM, Marti R, Karssemeijer N. Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One. 2014;9:e85952.CrossRefPubMedPubMedCentral

24.

Gweon HM, Youk JH, Kim JA, Son EJ. Radiologist assessment of breast density by BI-RADS categories versus fully automated volumetric assessment. AJR Am J Roentgenol. 2013;201:692–7.CrossRefPubMed

25.

Sartor H, Lang K, Rosso A, Borgquist S, Zackrisson S, Timberg P. Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists’ qualitative classification. Eur Radiol. 2016;26:4354–60.CrossRefPubMedPubMedCentral

26.

van der Waal D, den Heeten GJ, Pijnappel RM, Schuur KH, Timmers JM, Verbeek AL, et al. Comparing visually assessed BI-RADS breast density and automated volumetric breast density software: a cross-sectional study in a breast cancer screening setting. PLoS One. 2015;10:e0136667.CrossRefPubMedPubMedCentral

27.

Moshina N, Roman M, Waade GG, Sebuodegard S, Ursin G, Hofvind S. Breast compression parameters and mammographic density in the Norwegian Breast Cancer Screening Programme. Eur Radiol. 2018;28:1662–72.CrossRefPubMed

28.

Lau S, Ng KH, Abdul Aziz YF. Volumetric breast density measurement: sensitivity analysis of a relative physics approach. Br J Radiol. 2016;89:20160258.CrossRefPubMedPubMedCentral

29.

Youn I, Choi S, Kook SH, Choi YJ. Mammographic breast density evaluation in korean women using fully automated volumetric assessment. J Korean Med Sci. 2016;31:457–62.CrossRefPubMedPubMedCentral

30.

Machida Y, Saita A, Namba H, Fukuma E. Automated volumetric breast density estimation out of digital breast tomosynthesis data: feasibility study of a new software version. Springerplus. 2016;5:780.CrossRefPubMedPubMedCentral

31.

Suzuki A, Kuriyama S, Kawai M, Amari M, Takeda M, Ishida T, et al. Age-specific interval breast cancers in Japan: estimation of the proper sensitivity of screening using a population-based cancer registry. Cancer Sci. 2008;99:2264–7.CrossRefPubMed

32.

Destounis S, Johnston L, Highnam R, Arieno A, Morgan R, Chan A. Using volumetric breast density to quantify the potential masking risk of mammographic density. AJR Am J Roentgenol. 2017;208:222–7.CrossRefPubMed

33.

Holland K, van Gils CH, Mann RM, Karssemeijer N. Quantification of masking risk in screening mammography with volumetric breast density maps. Breast Cancer Res Treat. 2017;162:541–8.CrossRefPubMedPubMedCentral

Title: Can quantitative evaluation of mammographic breast density, “volumetric measurement”, predict the masking risk with dense breast tissue? Investigation by comparison with subjective visual estimation by Japanese radiologists
Authors: Mikinao Oiwa
Tokiko Endo
Namiko Suda
Takako Morita
Yasuyuki Sato
Tomonori Kawasaki
Shu Ichihara
Publication date: 01-05-2019
Publisher: Springer Japan
Published in: Breast Cancer / Issue 3/2019
Print ISSN: 1340-6868
Electronic ISSN: 1880-4233
DOI: https://doi.org/10.1007/s12282-018-0930-0

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 3/2019

Physical activity and physiotherapy: perception of women breast cancer survivors

Neoadjuvant chemotherapy and timing of sentinel lymph node biopsy in different molecular subtypes of breast cancer with clinically negative axilla

Intimacy and sexuality in women with breast cancer: professional guidance needed

Toll-like receptor 4 and breast cancer: an updated systematic review

Mutation screening of TP53, CHEK2 and BRCA genes in patients at high risk for hereditary breast and ovarian cancer (HBOC) in Brazil

p110α and p110β isoforms of PI3K are involved in protection against H2O2 induced oxidative stress in cancer cells

Keynote webinar | Spotlight on antibody–drug conjugates in cancer