Supplementary Material for: Combining Virtual Touch Tissue Imaging and BI-RADS May Improve Solid Breast Lesion Evaluation

<div>Background:</div><div> Ultrasound elastography (UE) is a novel imaging method. The purpose of this study was to determine the utility of Virtual Touch tissue imaging in the evaluation of solid breast lesions. Patients and Methods: 209 breast solid lesions in 192 patients that had been evaluated using ultrasound (US) and UE were reviewed and analyzed. </div><div>Results: The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for UE, US, and US plus UE in the differentiation of malignant from benign breast lesions were 80.8, 75.6, 77.9, 73.1, and 82.8% for UE, 87.2, 86.1, 86.6, 83.7, and 89.2% for US, and 92.5, 86.9, 89.5, 85.3, and 93.4% for US plus UE. There were significant differences between UE and US plus UE (all p < 0.05). Except for accuracy, there were no significant differences between UE and US or US and US plus UE (all p > 0.05). The area under the curve obtained from the ROC curve for UE, US, and US plus UE in differentiating malignant from benign lesions was 0.845, 0.884, and 0.922, respectively. Conclusion: The UE scoring system is not superior to BI-RADS in the sonographic evaluation of solid breast lesions. Combined use may improve the performance.</div

Supplementary Material for: A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model

<b><i>Objective:</i></b> The existing methods for identifying multiple rare variants underlying complex diseases in family samples are underpowered. Therefore, we aim to develop a new set-based method for an association study of dichotomous traits in family samples. <b><i>Methods:</i></b> We introduce a framework for testing the association of genetic variants with diseases in family samples based on a generalized linear mixed model. Our proposed method is based on a kernel machine regression and can be viewed as an extension of the sequence kernel association test (SKAT and famSKAT) for application to family data with dichotomous traits (F-SKAT). <b><i>Results:</i></b> Our simulation studies show that the original SKAT has inflated type I error rates when applied directly to family data. By contrast, our proposed F-SKAT has the correct type I error rate. Furthermore, in all of the considered scenarios, F-SKAT, which uses all family data, has higher power than both SKAT, which uses only unrelated individuals from the family data, and another method, which uses all family data. <b><i>Conclusion:</i></b> We propose a set-based association test that can be used to analyze family data with dichotomous phenotypes while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships