Quantitative analysis of breast parenchymal density: correlation with women's age

[Computerized estimation of a percent glandular tissue composition in computed radiography mammography]

Measurement of a percent glandular tissue composition (%GTC) is important in terms of the estimation of individual patient exposure dose and the prediction of malignancy, and thus a number of reports for estimating %GTC by use of a mammogram have been published. In this study, we propose a method for estimating individual %GTC by use of computed radiography (CR) mammograms. By employing breast-equivalent phantoms that are able to create breast phantom images with various combinations of fat and glandular tissue, as well as the thickness of whole breast, we determined a reference table for converting an each pixel value on CR mammography to the glandular tissue ratio. Therefore, the %GTC for individual breast was estimated by averaging glandular tissue ratio for a whole region. The clinical image data set that consisted of 49 CR mammograms were used for estimating %GTC. A paired comparison method for determining subjective ranking of the degree of breast density was employed in order to demonstrate the validity of our method. The results indicate that the average estimated %GTC was 35.0% (ranged from 12.0% to 67.0%) and they had a increased correlation with the ranking of those obtained by observer test. Therefore, it was suggested that our proposed method would be utilized for estimating the %GTC in objective manner.

[Mammographic evaluation of dense breasts: techniques and limits]

Breast density is a radiological concept based on the proportion of radiopaque glandular tissue relative to radiolucent fatty tissue. Mammographic evaluation of dense breasts is more difficult, related to technical difficulties, with decreased rates for detection and characterization of breast lesions, resulting in reduced sensitivity with increased number of interval cancers at routine follow-up when compared to radiolucent breasts. We will review the definition of dense breasts and their frequency, especially their relationship with the age of patients. We will discuss the current technical problems and the impact of breast density on the efficacy of conventional mammography. We will discuss the value of digital mammography, the role of computer assisted diagnosis (CAD) systems and tomosynthesis in the evaluation of dense breasts.

Age-Related Structural and Functional Changes in the Breast: Multimodality Correlation With Digital Mammography, Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography

Preliminary results generated from digital mammography, computed tomography, magnetic resonance imaging, and (18)F-fluorodeoxyglucose positron emission tomography demonstrate concordant findings of decreasing glandular tissue and decreasing metabolic activity with increasing age. These results are presented in the context of a detailed literature review summarizing age-related changes in the breast, both from the histologic/physiologic and the imaging perspectives. We also discuss potential applications of this approach and emphasize the importance of new advanced imaging technologies to offer high levels of quantitative precision for tissue characterization for research and clinical purposes.

Quantitative assessment of breast density from digitized mammograms into Tabar's patterns

We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.

Invasive Cancers Detected after Breast Cancer Screening Yielded a Negative Result: Relationship of Mammographic Density to Tumor Prognostic Factors

PURPOSE

To evaluate common breast tumor prognostic characteristics, including estrogen receptor (ER) status, grade, size, and method of detection, in relationship to mammographic density.

MATERIALS AND METHODS

The study involved 121 women who had negative results at both screening mammography and breast physical examination within 17 months before a diagnosis of breast cancer. Mammographic density was classified according to Breast Imaging Reporting and Data System patterns 1 through 4 (where 1 indicates a fatty breast and 4 indicates a dense breast). Axillary nodal status and tumor histologic ER status, histologic grade, size, stage, and method of detection (mammography alone, palpation alone, or both palpation and mammography) were analyzed by density category and tested for statistically significant differences across categories by using analysis of variance.

RESULTS

Statistically significant differences (P <.05) by density category were found for the following variables: ER positivity (15 of 15 tumors in category 1 breasts, 32 of 41 tumors in category 2 breasts, 37 of 49 tumors in category 3 breasts, and eight of 16 tumors in category 4 breasts were ER positive), occurrence of grade 1 tumors (eight, 11, 19, and four tumors in category 1, category 2, category 3, and category 4 breasts, respectively, were grade 1), mean tumor size (11.3, 13.0, 14.7, and 19.7 mm for category 1, category 2, category 3, and category 4 breasts, respectively), detection with mammography alone (13, 31, 36, and four tumors in category 1, category 2, category 3, and category 4 breasts, respectively, were detected with mammography alone), and occurrence of stage I tumors (10, 25, 28, and five tumors in category 1, category 2, category 3, and category 4 breasts, respectively, were stage I).

CONCLUSION

In women with negative results at clinical and mammographic screening within 17 months before breast tumor detection, subsequently diagnosed cancers tend to be ER negative, of higher grade, and larger in size in those with dense tissue patterns than in those with fat patterns.

Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences

The work presented herein is the second part of a review of breast tissue-related cancer-risk research. Briefly, in part 1, the tissue-risk research is discussed. In this part, factors that influence temporal breast tissue change are reviewed. Since breast composition is correlated with some of the known risk factors, understanding these influences may provide a mechanism for measuring the dynamics of breast cancer risk. The purpose of this work is to provide support for an automated serial mammography study under way at the authors' institution, where the digital mammographic images are acquired with a full-field digital mammography imaging system. At the initiation of the serial study, it was clear that the authors did not fully understand the nature of the problem: automatically comparing similar mammographic scenes acquired at different times. The evidence indicates that there are many factors that influence breast tissue at any given time and thus have the ability to alter the associated radiographic appearance over time. In general, the topics considered herein include aging; involution; breast development; exogenous and endogenous hormonal interactions such as hormone replacement therapy, oral contraceptive use, and menstrual timing; screening sensitivity issues and interval cancers; tumor growth rates; sojourn times; and lifestyle factors such as diet and exercise. Throughout this work, commentaries and suggestive strategies for automated serial image analysis are provided.

Automatic segmentation of mammographic density

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate a completely automatic method, based on Kittler's optimal threshold, to estimate breast density by using the mammographers' definition.

MATERIALS AND METHODS

Thirty-two normal, right-craniocaudal-view mammograms of women aged 37-86 years were digitized. The whole breast area was segmented by using Kittler's optimal threshold procedure, and the dense portions were then segmented by using a modified version of Kittler's method. Segmentation results were validated by three independent mammographers who provided a signed percentage (in steps of 5%) to indicate the difference between their own visual estimation of the dense portions and the results obtained with the algorithm. The difference between the algorithm measurements and the mammographers' measurements was compared to the interobserver differences.

RESULTS

A high correlation was found between the algorithm measured density and the mammographers' measurements. Spearman correlations ranged from 0.92 to 0.95 (P < .001). Algorithm-measured density differed from the mammographers' measurements by an average of 6.9% (ie, average of the absolute differences). In contrast, mammographers' measurements differed between themselves by an average of 5.4%.

CONCLUSION

The difference between density as measured with the algorithm and as measured by the mammographers is similar to the differences observed between mammographers. This algorithm could be useful in providing clinically accurate estimates of breast density.