The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume

Purpose: Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Materials and Methods: Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. Results: For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m2. Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10−9) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume. Conclusions: Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.ECU Open Access Publishing Support Fun

Residual convolutional neural networks to automatically extract significant breast density features

In this paper, we present a work on breast density classification performed with deep residual neural network and we discuss the future analysis we could perform. Breast density is one of the most important breast cancer risk factor and it represents the amount of fibroglandular tissue with respect to fat tissue as seen on a mammographic exam. However, it is not easy to include it in risk models because of its variability among women and its qualitative definition. We trained a deep CNN to perform breast density classification in two ways. First, we classified mammograms using two “super-classes” that are dense and non-dense breast. Second, we trained the residual neural network to classify mammograms according to the four classes of the BI-RADS standard. We obtained very good results compared to our literature knowledge in terms of accuracy and recall. In the near future, we are going to improve the robustness of our algorithm with respect to the mammographic systems used and we want to include pathological exams too. Then we want to study and characterize the CNN-extracted features in order to identify the most significant for breast density. Finally, we want to study how to quantitatively measure the precision of the network in capturing the significative part of the images

Breast density quantification with cone-beam CT: a post-mortem study

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The percent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson’s r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate (SEE) was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation