Abstract
It is crucial to assess the fibroglandular breast tissue density to define the degree of the risk that the healthy breast tissue will obscure the lesions. Subjective assessment criteria, proceed by the reading physicians by using the mammary gland concentrations on mammograms, are defined as the breast classification method. However, due to the existence of between observer’s variability, a computer-based quantitative classification method is required. The conventional method classifies according to the ratio of the Dmg region (mammary gland region) to the Dc region (fibroglandular breast tissue region). However, this does not include subjective evaluation elements. The purpose of this study is to improve the concordance rate with the subjective assessment by performing an automated classification based on image similarity. First, 130 cases of right MLO (Medio-Lateral Oblique) images, subjectively classified as fatty tissue, mammary gland diffuseness, non-uniform high density, and high density, were reclassified to two groups; fatty tissue and mammary gland diffuseness as Non-Dense breast, and non-uniform high density and high density as Dense breast. Next, as for evaluation images, 33 cases of both sides MLO images taken by different mammography devices were used. Finally, the image similarity analysis result using Normalized Cross-Correlation between the search image and the evaluation image was derived, and the degree of coincidence of subjective breast classification was calculated. As a result, the concordance rate between the conventional method and the subjective evaluation results of this method improved from 73 % to 91 %, and the kappa coefficient improved from 0.49 to 0.81. This result indicates that our approach is more useful for the automated classification of mammograms based on fibroglandular breast tissue density.
Highlights
The relationship between breast density and breast cancer risk was the first to reported in 1976 by Wolf et al [1,2,3]
The concordance rate between the conventional method and the subjective evaluation results of this method improved from 73 % to 91 %, and the kappa coefficient improved from 0.49 to 0.81
Evaluation of the fibroglandular breast tissue density has been based on the subjective evaluation of observers that Wolfe classification, Breast Imaging Reporting and Data System (BI-RADS) and Tabár classification [2, 11, 12]
Summary
The relationship between breast density and breast cancer risk was the first to reported in 1976 by Wolf et al [1,2,3]. Several studies show that the risk of developing breast cancer is two to six times higher for women with dense breasts than women in the lowest density category [4,5,6,7]. These studies suggested that, with the introduction of digital mammography, breast density classification has become more difficult, and this means that high-density mammary glands may hide breast cancer [8,9,10]. Previous studies showed a considerable interand intra-reader variability when using BI-RADS [13, 14] To correct these variabilities, a semi-automated and automated method to quantify breast density has been studied [15,16,17]. The accuracy of the breast density assessment software on the market has been reported [18, 19]
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