Examples of ‘parenchyma rate in’ parameter (also Fig 3B) maps from a non-triple-negative (TN) patient (left) and a TN patient (right) illustrating the difference of a statistical texture feature between members of the two groups in image form.

<p>Slices of the ‘parenchyma rate in’ parameter map void of tumor tissue are presented in the sagittal plane. It is evident the variation of this background parenchymal enhancement texture feature’s value is greater in TN cancers, where standard deviation is markedly higher at 352.9 as opposed to 133.8 in the non-TN patient.</p

Box plots illustrating differences in distributions (quartiles as red boxes, grand mean indicated as spanning line) of the three most predictive quantitative features found in differentiation tasks: the lesion’s ‘mass size’ feature (a), parenchyma’s ‘skewness of Signal Enhancement Ratio’ feature (b), and parenchyma’s ‘standard deviation of rate in’ feature (c) compared between the triple-negative (TN) and non-TN groups.

<p>p-values were calculated by Wilcoxon Mann-Whitney tests.</p

Example of tissue segmentation performed of all cancer patients’ affected breast images.

<p>At top left (a), a dynamic contrast-enhanced MRI exam at t3 is seen in the axial plane, illustrating one slice of the view used for contouring the breast and tumor. At top right (b), the result of breast segmentation is shown. At bottom left (c), the segmented tumor is highlighted in blue. Finally at bottom right (d), the parenchyma segmented at t1 is highlighted in pink. Breast subcompartment segmentation was performed in 3-dimensions.</p

Summary of radiomic analysis performed in this study.

<p>Clinical features were evaluated by a radiologist according to Breast Imaging Reporting and Data System directly from dynamic contrast-enhanced MRI (a). 3-Dimensional tumor (red) and parenchyma (light blue) compartments were segmented (b), from which volumetric breast density was immediately estimated (c). Enhancement maps were then generated (d), from which textural features of tissue compartments were extracted and defined as enhancement heterogeneity (e). Subsequently, two analyses were conducted using extracted features: supervised learning of breast cancer subtype was performed with a support vector machine classifier (f) and unsupervised learning of background parenchymal enhancement feature expression pattern was performed with <i>k</i>-means clustering (g).</p

Unsupervised <i>k</i>-means clustering of breast cancer patients (n = 88) on the x-axis and quantitative background parenchymal enhancement (BPE) feature expression (n = 39) on y-axis (as z-scores, with scale at bottom left. std = standard deviation).

<p>Correspondence of patient groups with similar radiomic expression patterns can be seen where the majority of triple-negative (TN) breast cancers have grouped together in the left cluster (9 of 11 TN in partition highlighted orange at top left) due to association of the BPE heterogeneity feature signatures. 1^st order statistical texture features are highlighted as purple and similarly 2^nd order statistical texture features are green at right indicating correspondence of feature groups with clustered expression patterns.</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

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

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

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

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