Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation.

AIM To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification. MATERIALS AND METHODS Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale. RESULTS The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial". CONCLUSION The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation.

[1]  Wolfgang Bogner,et al.  Introduction of an Automated User–Independent Quantitative Volumetric Magnetic Resonance Imaging Breast Density Measurement System Using the Dixon Sequence: Comparison With Mammographic Breast Density Assessment , 2015, Investigative radiology.

[2]  S. Duffy,et al.  Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. , 2011, Journal of the National Cancer Institute.

[3]  C. Klifa,et al.  Quantification of breast tissue index from MR data using fuzzy clustering , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  R. Birdwell,et al.  Breast density: clinical implications and assessment methods. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[5]  J. Wolfe.  Breast parenchymal patterns and their changes with age. , 1976, Radiology.

[6]  Kamila Czene,et al.  Automated Measurement of Volumetric Mammographic Density: A Tool for Widespread Breast Cancer Risk Assessment , 2014, Cancer Epidemiology, Biomarkers & Prevention.

[7]  V. McCormack,et al.  Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis , 2006, Cancer Epidemiology Biomarkers & Prevention.

[8]  Karla Kerlikowske,et al.  The Impact of Breast Density on Breast Cancer Risk and Breast Screening , 2012, Current Breast Cancer Reports.

[9]  S. Ciatto,et al.  Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. , 2005, Breast.

[10]  Örjan Smedby,et al.  Quantitative abdominal fat estimation using MRI , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Örjan Smedby,et al.  Consistent intensity inhomogeneity correction in water–fat MRI , 2015, Journal of magnetic resonance imaging : JMRI.

[12]  M. Borga,et al.  Dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment in vivo , 2016, Oncoimmunology.

[13]  Magnus Borga,et al.  Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system , 2014, European Radiology.

[14]  M. Borga,et al.  Men develop more intraabdominal obesity and signs of the metabolic syndrome after hyperalimentation than women. , 2009, Metabolism: clinical and experimental.

[15]  Berkman Sahiner,et al.  Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images. , 2004, Medical physics.

[16]  P. Narula.  MAMMOGRAPHIC DENSITY AND THE RISK AND DETECTION OF BREAST CANCER , 2016 .

[17]  Magnus Borga,et al.  Automatic and quantitative assessment of regional muscle volume by multi‐atlas segmentation using whole‐body water–fat MRI , 2015, Journal of magnetic resonance imaging : JMRI.

[18]  Magnus Borga,et al.  Validation of a fast method for quantification of intra‐abdominal and subcutaneous adipose tissue for large‐scale human studies , 2015, NMR in biomedicine.

[19]  Hans Knutsson,et al.  Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient , 2007, MICCAI.

[20]  Magnus Borga,et al.  MANA - Multi scale adaptive normalized averaging , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[21]  X. Castells,et al.  Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. , 2012, The British journal of radiology.

[22]  Heang-Ping Chan,et al.  Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories. , 2006, Radiology.

[23]  Michael Khazen,et al.  A Pilot Study of Compositional Analysis of the Breast and Estimation of Breast Mammographic Density Using Three-Dimensional T1-Weighted Magnetic Resonance Imaging , 2008, Cancer Epidemiology Biomarkers & Prevention.

[24]  Jianjun Liu,et al.  Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement , 2011, Breast Cancer Research.

[25]  Catherine Klifa,et al.  Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort. , 2010, Magnetic resonance imaging.

[26]  N. Boyd,et al.  The quantitative analysis of mammographic densities. , 1994, Physics in medicine and biology.