Reproducibility of mammographic classifications

Myers, LE; McLelland, Robert; Stricker, CX; Sa, Feig; Martin, J E; Moskowitz, Myron; Nielsen, M.

doi:10.2214/ajr.141.3.445

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…The boundaries between Wolfe grades are based on arbitrary choices regarding the relative importance of these two factors. In addition, the assessment of Wolfe grade is subjective and has a large degree of inter-and intra-observer variability (Carlile et a1 1983, Myers et a1 1983. Given this variability, recommendations for patient follow-up based on Wolfe grade are likely to be highly observerdependent.…”

Section: Drawbacks To Wolfe Gradementioning

confidence: 99%

Characterisation of mammographic parenchymal pattern by fractal dimension

Caldwell

Stapleton²,

Holdsworth³

et al. 1990

Phys. Med. Biol.

211

102

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A consistent, quantitative, observer-independent method of characterising mammographic parenchymal pattern is described. The method is based on the calculation of the 'fractal dimension' of digitised mammograms. The degree of correlation between the parenchymal pattern classifications by a fractal-based system and those of radiologists is assessed. For a set of 70 mammograms, average weighted proportion agreement among three radiologists in calling Wolfe grades was 85%, while agreement between the radiologists and our fractal classifier was 84%. The method developed may prove to be useful in establishing an index of risk for breast cancer and, ultimately, in determining intervals between examinations for individuals in a mammographic screening programme.

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Section: Drawbacks To Wolfe Gradementioning

confidence: 99%

Characterisation of mammographic parenchymal pattern by fractal dimension

Caldwell

Stapleton²,

Holdsworth³

et al. 1990

Phys. Med. Biol.

211

102

View full text Add to dashboard Cite

show abstract

“…Wolfe reported that women in the highest risk class DY has 37 times higher risk of developing breast cancer compared to those in the lowest risk class N1. 102 Nevertheless, his results could not be replicated by other researchers [117][118][119][120][121][122] possibly due to inter-rater variations interpreting such subjective descriptions. Many years later, in 1997, Tabár 123 defined patterns of glandular distributions based on histologic-mammographic correlations, and on an understanding of the building blocks of the breast.…”

Section: Breast Density Estimation Using Patternsmentioning

confidence: 67%

“…However, these patterns have not been widely accepted again because of subjectivity and difficulty in assessing them. 110,[117][118][119][120][122][123][124][125][126][127][128][129][130] Table I summarizes the pattern-based approaches. Interestingly, these methods were not driven by any sense of genuine physical measurement and ground truth was purely focused on outcomes-i.e., how well did a particular pattern predict risk of cancer development?…”

Section: Breast Density Estimation Using Patternsmentioning

confidence: 99%

Vision 20/20: Mammographic breast density and its clinical applications

Lau

2015

Medical Physics

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Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

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“…This subjective classification scheme is limited by its considerable intrareader and inter-reader variability. 2 Several groups have reported more quantitative approaches [3][4][5] for measuring breast density. Area-based techniques have included qualitative and semiquantitative classification schemes, 3,6,7 and quantitative estimations from manual or semimanual segmentation of a digital image histogram.…”

Section: Introductionmentioning

confidence: 99%

Quantification of breast density with dual energy mammography: An experimental feasibility study

Ducote

Molloi

2010

Medical Physics

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Purpose: Breast density, the percentage of glandular breast tissue, has been shown to be a strong indicator of breast cancer risk. A quantitative method to measure breast density with dual energy mammography was investigated using physical phantoms. Methods: The dual energy mammography system used a tungsten anode x-ray tube with a 50 m rhodium beam filter for low energy images and a 300 m copper beam filter for high energy images. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Four different phantom studies were used to evaluate the technique. The first study consisted of phantoms with thicknesses of 2.5-8.5 cm in 0.5 cm steps with variable densities centered at a mean of 28%. The second study consisted of phantoms at a fixed thickness of 4.0 cm, which ranged in densities from 0% to 100% in increments of 12.5%. The third study consisted of 4.0 cm thick phantoms at densities of 25%, 50% and 75% each imaged at three areal sizes, approximately 62.5, 125, and 250 cm 2 , in order to assess the effect of breast size on density measurement. The fourth study consisted of step phantoms designed to more closely mimic the shape of a female breast with maximal thicknesses from 3.0 to 7.0 cm at a fixed density of 50%. All images were corrected for x-ray scatter. Results: The RMS errors in breast density measurements were 0.44% for the variable thickness phantoms, 0.64% for the variable density phantoms, 2.87% for the phantoms of different areal sizes, and 4.63% for step phantoms designed to closely resemble the shape of a breast. Conclusions:The results of the phantom studies indicate that dual energy mammography can be used to measure breast density with an RMS error of approximately 5%.

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Reproducibility of mammographic classifications

Cited by 9 publications

References 9 publications

Characterisation of mammographic parenchymal pattern by fractal dimension

Characterisation of mammographic parenchymal pattern by fractal dimension

Vision 20/20: Mammographic breast density and its clinical applications

Quantification of breast density with dual energy mammography: An experimental feasibility study

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