Breast Cancer Risk Associations with Digital Mammographic Density by Pixel Brightness Threshold and Mammographic System

Abstract

Purpose To compare three mammographic density measures defined by different pixel intensity thresholds as predictors of breast cancer risk for two different digital mammographic systems. Materials and Methods The Korean Breast Cancer Study included 398 women with invasive breast cancer and 737 control participants matched for age at mammography (±1 year), examination date, mammographic system, and menopausal status. Mammographic density was measured by using the automated Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software and the semiautomated Cumulus software at the conventional threshold (Cumulus) and at increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were Box-Cox-transformed and adjusted for age, body mass index, and menopausal status. Conditional logistic regression was used to estimate risk associations. For calculation of measures of predictive value, the change in odds per standard deviation (OPERA) and the area under the receiver operating characteristic curve (AUC) were used. Results For dense area, with use of the direct conversion system the OPERAs were 1.72 (95% confidence interval [CI]: 1.38, 2.15) for LIBRA, 1.58 (95% CI: 1.27, 1.97) for Cumulus, 2.04 (95% CI: 1.60, 2.59) for Altocumulus, and 3.48 (95% CI: 2.45, 4.47) for Cirrocumulus (P < .001). The corresponding AUCs were 0.70, 0.69, 0.76, and 0.89, respectively. With use of the indirect conversion system, the corresponding OPERAs were 1.50 (95% CI: 1.28, 1.76), 1.36 (95% CI: 1.16, 1.59), 1.40 (95% CI: 1.19, 1.64), and 1.47 (95% CI: 1.25, 1.73) (P < .001) and the AUCs were 0.64, 0.60, 0.61, and 0.63, respectively. Conclusion It is possible that mammographic density defined by higher pixel thresholds could capture more risk-predicting information with use of a direct conversion mammographic system; the mammographically bright, rather than white, regions are etiologically important. RSNA, 2017.

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

“…The risk discrimination from conventional mammographic density compares favourably with that achieved by the latest polygenic risk scores for which the OPERA is about 1.6 [9]. Our new mammogram-based risk measures have OPERAs of 1.6 or higher [8,[10][11][12][13][14] and we will now discuss how this came about.…”

“…The risk estimates for Altocumulus were often little different to those for Cumulus, but the risk estimates for Cirrocumulus in particular were in general stronger, especially for Korean women (who are diagnosed on average a decade or so earlier than Australian women) [10] and for Australian women diagnosed at a younger age [11]. These findings applied to film and well as digital mammograms, and for the latter, appeared to depend on the manufacturer [12].…”

“…We found from multiple studies of Australian and Korean women [10][11][12][13][14] that the new measures of mammographic density, Altocumulus and Cirrocumulus, did better than Cumulus at predicting risk. One notable exception is risk of interval breast cancer [14].…”

Comparison of Some Statistical Measurements Extracted From Benign, Malignant and Normal MRI Brain Images

“…Normal tissues give structured information about the intensity of light in one pixel, while we notice the differences between the information shown on the images of people with benign diseases or malignant diseases, as well as differences between the apparent information between benign diseases on the side and malignant diseases on the other [12]. Recently, brain tumor classifications have been used through magnetic resonance imaging (MRI) and the use of basic statistical information accordingly, including mean, standard deviation and variance, and then subjecting them to filtering, classification, and segmentation of images to extract tumor location and his size [13].…”

Looking beyond the mammogram to assess mammographic density: A narrative review

“…Although also associated with increased BC risk [80,128], percent MD (PMD), which is the proportion of MD area as a function of the total breast area, was recently shown to better predict masking of interval cancers [72]. Higher brightness thresholding using the Cumulus software resulted in better risk prediction with successively higher brightness thresholds, dubbed Altocumulus and Cirrocumulus [84][85][86]. When adjusted for other risk factor interactions using OPERA, the risk associated with MD is similar to, or higher than that seen with known gene mutations and family history [53].…”