Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study

Gubern-Mérida, Albert; Kallenberg, Michiel; Platel, Bram; Mann, Ritse M.; Martí, Robert; Karssemeijer, Nico

doi:10.1371/journal.pone.0085952

Cited by 114 publications

(106 citation statements)

References 27 publications

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“…This is likely due to better estimation of the amount of fat by the computer algorithm compared to the visual assessment and is consistent with the commercially available automated breast density assessment program for mammography (16)(17)(18). In addition, the 3D MRI likely includes larger area of fatty breast located in the retro-glandular and lower axillary regions yielding lower overall percentage of the quantified FGT.…”

Section: Discussionsupporting

confidence: 64%

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study

Mema

Guo

et al. 2016

Quant. Imaging Med. Surg.

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Background: The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value.Methods: An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an inhouse, proprietary segmentation algorithm which combines the region-based active contours and a level Conclusions: Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized measurements with limited intra or inter-observer variability.

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Section: Discussionsupporting

confidence: 64%

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study

Mema

Guo

et al. 2016

Quant. Imaging Med. Surg.

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“…3 Women with high MD might benefit from additional imaging, such as ultrasound (US) or magnetic resonance imaging (MRI), or more frequent 65 screening compared with women with low MD. [4][5][6][7] There are several methods to measure MD [8][9][10][11][12][13] , the most common of which is visual assessment by radiologists using the Breast Imaging Reporting and Data System (BIRADS) scale. 8 This system is prone to inter and intra-reader variability 14,15 and thus several 70 computer-assisted methods have been developed including Cumulus 9 and Madena 10 .…”

Section: Introductionmentioning

confidence: 99%

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

Waade

Highnam²,

Hauge

et al. 2016

Medical Physics

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2 AbstractPurpose: Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending 35 additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation, however the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. 40Methods: Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased to simulate measurement error. Increments of 1mm were used up to ±15% error of recorded CBT was achieved. New images were created for each 1mm step in thickness resulting in a total of 974 images which then had Volpara Density Grade (VDG) and volumetric density percentage 45 assigned. Results: A change in VDG was recorded in 38.5% (n= 20) of mammograms when applying ±15% error to the recorded CBT and 11.5 % (n= 6) were within the QC standard prescribed error of ±5mm. Conclusion:The current QC standard of ±5mm error in recorded CBT creates the potential 50 for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered.

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“…The software generates four volumetric density grades (VDGA -D) corresponding to the four ACR BIRADS breast density categories ( Figure 4A). [123][124][125] Volpara has demonstrated a strong positive correlation with BIRADS, 124,126 and has been shown to be more reliable and reproducible, 39,117,118 and a better risk predictor from breast density assessment 28 than other mammographic breast density assessment tools. Currently, there is increased advocacy for breast density notification in the United States, 127 and 22…”

Section: Categorisation Of Breast Composition Using "Quantitative Arementioning

confidence: 99%

“…121,122 Volpara TM (Matakina Technology Limited) is also based on relative physics principles, and measures mammographic breast density by finding a reference point of entirely fat (P FAT ) in each image and then estimating X-ray attenuation relative to that point for all other points in the image. 123 Volpara calculates volume of dense tissue by integrating the thickness of dense tissue at each pixel level values over the image; it then computes the volume of the breast by multiplying the area of the breast by the recorded breast thickness. 124 It calculates percentage volumetric breast density as a percentage ratio of the volume of fibroglandular and the total volume of the breast.…”

Section: Categorisation Of Breast Composition Using "Quantitative Arementioning

confidence: 99%

Breast composition: Measurement and clinical use

Ekpo¹,

Hogg²,

Highnam³

et al. 2015

Radiography

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Breast density is a measure of the extent of radiodense fibroglandular tissue in the breast. The risk of developing breast cancer and the risk of missing cancer at screening rise with higher breast density. In this paper, the historical background to breast density measurement is outlined and current evidence based practice is explained. The relevance of breast density knowledge to mammographic practice and image interpretation is considered in the light of clinical assessment and notification of mammographic breast density (MBD). The current work also discusses risk stratification for decisionmaking regarding screening frequency and better modalities for earlier detection of breast cancer in the dense breast. Automated volumetric approaches are explained while ultrasound, digital breast tomosynthesis, molecular breast imaging, and magnetic resonance imaging are introduced as valuable adjuncts to digital mammography for imaging the dense breast. The work concludes on the important note that screened women should be notified of their breast density, and such notification should be accompanied with clear and adequate information about breast density and cancer risk, strategies associated with lower MBD, as well as best screening intervals and pathways for women with dense breasts. Adoption of these strategies may be crucial to early detection and treatment of cancer and improving survival from the disease.

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Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study

Cited by 114 publications

References 27 publications

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

Breast composition: Measurement and clinical use

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