Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990–0.997); for breast volume, r = 0.985 (95% CI 0.934–0.994); and for FGT volume, r = 0.979 (95% CI 0.958–0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819–0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630–0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual’s breast cancer risk stratification.

[1]  Nico Karssemeijer,et al.  Robust Breast Composition Measurement - VolparaTM , 2010, Digital Mammography / IWDM.

[2]  Daniel B Kopans,et al.  Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. , 2008, Radiology.

[3]  T. Sellers,et al.  Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications. , 2014, Academic radiology.

[4]  Karla Kerlikowske,et al.  Agreement of Mammographic Measures of Volumetric Breast Density to MRI , 2013, PloS one.

[5]  Nico Karssemeijer,et al.  Volumetric breast density estimation from full-field digital mammograms , 2006, IEEE Trans. Medical Imaging.

[6]  Eralda Mema,et al.  Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study. , 2016, Quantitative imaging in medicine and surgery.

[7]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

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

[9]  T. Helbich,et al.  Accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue using MRI: correlation with anthropomorphic breast phantoms , 2017, NMR in biomedicine.

[10]  J. Wolfe.  Breast patterns as an index of risk for developing breast cancer. , 1976, AJR. American journal of roentgenology.

[11]  Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm , 2014, International journal of breast cancer.

[12]  Nico Karssemeijer,et al.  Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework , 2015, IEEE Journal of Biomedical and Health Informatics.

[13]  T. Sellers,et al.  Mammographic density, breast cancer risk and risk prediction , 2007, Breast Cancer Research.

[14]  Roberto Orecchia,et al.  Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. , 2010, European journal of cancer.

[15]  Olga Chambers,et al.  Automated breast-region segmentation in the axial breast MR images , 2015, Comput. Biol. Medicine.

[16]  Alberto Tagliafico,et al.  Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one. , 2009, Breast.

[17]  G. Gulsen,et al.  Imaging Breast Density: Established and Emerging , 2015 .

[18]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[19]  G. Gulsen,et al.  Imaging Breast Density: Established and Emerging Modalities1 , 2015, Translational oncology.

[20]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[21]  M. Könönen,et al.  Incidentally detected enhancing lesions found in breast MRI: analysis of apparent diffusion coefficient and T2 signal intensity significantly improves specificity , 2016, European Radiology.

[22]  D. Hinkle,et al.  Applied statistics for the behavioral sciences , 1979 .

[23]  T. Helbich,et al.  Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment , 2016, European Radiology.

[24]  Manuela Durando,et al.  A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification. , 2012, Breast.

[25]  Ruey-Feng Chang,et al.  Comparative study of density analysis using automated whole breast ultrasound and MRI. , 2011, Medical physics.

[26]  Chang Oan Sung,et al.  An improved method of breast MRI segmentation with simplified K-means clustered images , 2011, RACS.

[27]  Min-Ying Su,et al.  Impact of skin removal on quantitative measurement of breast density using MRI. , 2010, Medical physics.

[28]  Camille Arianne Dollins,et al.  An evaluation of breast radiological density assessment by Quantra software as compared to visual classification , 2015 .

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

[30]  Alberto Tagliafico,et al.  Comparative estimation of percentage breast tissue density for digital mammography, digital breast tomosynthesis, and magnetic resonance imaging , 2013, Breast Cancer Research and Treatment.

[31]  A. Juette,et al.  Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation. , 2017, Clinical radiology.

[32]  Nico Karssemeijer,et al.  Using deep learning to segment breast and fibroglandular tissue in MRI volumes , 2017, Medical physics.

[33]  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.

[34]  Amro Masarwah,et al.  Very low mammographic breast density predicts poorer outcome in patients with invasive breast cancer , 2015, European Radiology.

[35]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[36]  Mireille J. M. Broeders,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 , 2015, PloS one.

[37]  B. Keller,et al.  Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures. , 2017, Radiology.

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

[39]  Min-Ying Su,et al.  Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. , 2008, Medical physics.

[40]  H Rusinek,et al.  Fatty and fibroglandular tissue volumes in the breasts of women 20-83 years old: comparison of X-ray mammography and computer-assisted MR imaging. , 1997, AJR. American journal of roentgenology.

[41]  Anders Tingberg,et al.  Breast tomosynthesis: Accuracy of tumor measurement compared with digital mammography and ultrasonography , 2010, Acta radiologica.

[42]  N. Boyd,et al.  Mammographic density and breast cancer risk: current understanding and future prospects , 2011, Breast Cancer Research.

[43]  Emily F. Conant,et al.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging. , 2016, Radiology.