Best linear regression fit line with 95% confidence interval bands for percentage fibroglandular density (top), log fibroglandular volume (middle), and total breast volume (bottom) for MRI versus either SXA (left), Quantra (center), or Volpara (right) measures.

<p>Solid points correspond to example images in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081653#pone-0081653-g004" target="_blank">Figure 4</a>.</p

Six comparisons of the LCC mammograms to their respective left central breast axial-slice MR images on five different women (c and d are the same woman).

<p>The white line connecting points in the MR images define the total breast volume. The MRI fibroglandular volume is shown delineated with white lines without points. Solid data points 4a–4f in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081653#pone-0081653-g001" target="_blank">Figure 1</a> correspond to the image labels a–f. Compared to the mammographically-derived SXA values, a) MRI percent density is higher, b) MRI percent density is lower, c) MRI breast volume is higher, d) of the same woman as c (this mammogram not part of analyses, only here and measures plotted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081653#pone-0081653-g001" target="_blank">Figure 1</a> to illustrate one reason for discrepancy between methods' results), MRI breast volume is better segmented due to the breast being extended more into the mammographic image field, e) MRI breast volume is lower, f) all MRI measures of density and volume were in substantial agreement.</p

Validation of SXA model using breast biology and adipose volume estimates from MRI.

<p>The amount of water volume in the MRI adipose volume was estimated to be 15% of the volume, which is consistent with previous work estimating it to be between 8% <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081653#pone.0081653-Boyd1" target="_blank">[34]</a> and 20% <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081653#pone.0081653-Khazen1" target="_blank">[35]</a>. The MRI model does not include adipose density in the fibroglandular volume while SXA does. Subtracting out the adipose water volume from the SXA fibroglandular volume improved the agreement between SXA and MRI from R² = 0.78 to 0.83 and removed most of the bias between the measures.</p

Percent fibroglandular density quartile ranges by method (n = 99).

<p>Percent fibroglandular density quartile ranges by method (n = 99).</p

Comparison of quartiles classification for percent fibroglandular density (top) and log fibroglandular volume (middle) for MRI versus SXA (left), Quantra (center), and Volpara (right).

<p>The bottom row of plots show quartiles comparisons between mammographic density measures. Legend at right defines categories of agreement, where either the two compared method's agree completely (black) or are off by one or two quartiles up or down in comparison with the other method.</p

Additional file 2: Figure S1. of Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status

Dendrogram of cluster analysis of the top 15 features with PD, age and BMI. Similar features cluster together. Percent density groups closely with BMI and age. The figure is restricted to the cases. (PDF 76 kb

Additional file 1: Table S1. of Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status

Baseline characteristics of study population per study site. Table S2. Pearson correlation coefficient for top 15 significant features. Correlations calculated using case subjects. Gray and gray with line pattern highlight strength of positive and negative associations, respectively. (PDF 123 kb

Relationship of circulating insulin-like growth factor-I and binding proteins 1-7 with mammographic density among women undergoing image-guided diagnostic breast biopsy.

BACKGROUND: Mammographic density (MD) is a strong breast cancer risk factor that reflects fibroglandular and adipose tissue composition, but its biologic underpinnings are poorly understood. Insulin-like growth factor binding proteins (IGFBPs) are markers that may be associated with MD given their hypothesized role in breast carcinogenesis. IGFBPs sequester IGF-I, limiting its bioavailability. Prior studies have found positive associations between circulating IGF-I and the IGF-I:IGFBP-3 ratio and breast cancer risk. We evaluated the associations of IGF-I, IGFBP-3, and six other IGFBPs with MD. METHODS: Serum IGF measures were quantified in 296 women, ages 40-65, undergoing diagnostic image-guided breast biopsy. Volumetric density measures (MD-V) were assessed in pre-biopsy digital mammograms using single X-ray absorptiometry. Area density measures (MD-A) were estimated by computer-assisted thresholding software. Age, body mass index (BMI), and BMI RESULTS: IGF-I and IGFBP-3 were not strongly associated with MD after BMI adjustment. In multivariable analyses among premenopausal women, IGFBP-2 was positively associated with both percent MD-V (β = 1.49, p value = 0.02) and MD-A (β = 1.55, p value = 0.05). Among postmenopausal women, positive relationships between IGFBP-2 and percent MD-V (β = 2.04, p = 0.003) were observed; the positive associations between IGFBP-2 and percent MD-V were stronger among lean women (BMI  CONCLUSIONS: In this comprehensive study of IGFBPs and MD, we observed a novel positive association between IGFBP-2 and MD, particularly among women with lower BMI. In concert with in vitro studies suggesting a dual role of IGFBP-2 on breast tissue, promoting cell proliferation as well as inhibiting tumorigenesis, our findings suggest that further studies assessing the role of IGFBP-2 in breast tissue composition, in addition to IGF-1 and IGFBP-3, are warranted.</p

Mammary collagen architecture and its association with mammographic density and lesion severity among women undergoing image-guided breast biopsy

Background: Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. Methods: Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm2)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. Results: Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p Conclusions: Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.</p

Elliptic systems with anisotropic potential: existence and regularity of solutions

We briefly summarize existing result in theory of minimizers of elliptic variational functionals. We introduce proof of existence and regularity such functional under assumpti- ons of quaziconvexity and izotrophic growth estimates, and discuss possible generalization to anizotropic case. Our proof is a compilation from more sources, modified in order of simplicity, readability and detailed analysis of all steps