Additional file 2 of Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

Table S2: The top 10 significant genes based on KDDN and dwgLASSO without prior biological knowledge incorporated. (CSV 4.00 kb

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

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

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