First step to facilitate long term and multi centre studies of shear wave elastography in solid breast lesions using a computer assisted algorithm

Purpose: Shear wave elastography (SWE) visualises the elasticity of tissue. As malignant tissue is generally stiffer than benign tissue, SWE is helpful to diagnose solid breast lesions. Until now, quantitative measurements of elasticity parameters have been possible only, while the images were still saved on the ultrasound imaging device. This work aims to overcome this issue and introduces an algorithm allowing fast offline evaluation of SWE images. Methods: The algorithm was applied to a commercial phantom comprising three lesions of various elasticities and 207 in vivo solid breast lesions. All images were saved in DICOM, JPG and QDE (quantitative data export; for research only) format and evaluated according to our clinical routine using a computer-aided diagnosis algorithm. The results were compared to the manual evaluation (experienced radiologist and trained engineer) regarding their numerical discrepancies and their diagnostic performance using ROC and ICC analysis. Results: ICCs of the elasticity parameters in all formats were nearly perfect (0.861–0.990). AUC for all formats was nearly identical for ${E}_{\mathrm{max}}$ and ${E}_{\mathrm{mean}}$ (0.863–0.888). The diagnostic performance of SD using DICOM or JPG estimations was lower than the manual or QDE estimation (AUC 0.673 vs. 0.844). Conclusions: The algorithm introduced in this study is suitable for the estimation of the elasticity parameters offline from the ultrasound system to include images taken at different times and sites. This facilitates the performance of long-term and multi-centre studies

Elevated serum levels of soluble CD154 in children with juvenile idiopathic arthritis

<p>Abstract</p> <p>Objective</p> <p>Cytokines play important roles in mediating inflammation in autoimmunity. Several cytokines are elevated in serum and synovial fluid samples from children with Juvenile Idiopathic Arthritis (JIA). Soluble CD154 (sCD154) is elevated in other autoimmune disorders, but has not been characterized in JIA. Our objectives were to determine if sCD154 is elevated in JIA, and to examine correlations between sCD154 and other inflammatory cytokines.</p> <p>Methods</p> <p>Serum from 77 children with JIA and 81 pediatric controls was analyzed for interleukin (IL)1β, IL2, IL4, IL5, IL6, IL8, IL10, IL12, IL13, sCD154, interferon-γ (IFNγ), soluble IL2 receptor (sIL2R), and tumor necrosis factor-α (TNFα), using the Luminex Multi-Analyte Profiling system. Differences in levels of cytokines between cases and controls were analyzed. Logistic regression was also performed.</p> <p>Results</p> <p>sCD154 was significantly elevated in cases compared to controls (p < 0.0001). IL1β, IL5, IL6, IL8, IL13, IFNγ, sIL2R, and TNFα were also significantly elevated in JIA. Levels of sCD154 were highly correlated with IL1β, IL6, IL8, and TNFα (p < 0.0001). Logistic regression analysis suggested that IL6 (odds ratio (OR): 1.4, p < 0.0001), sCD154 (OR: 1.1, p < 0.0001), and TNFα (OR: 1.1, p < 0.005) were positively associated with JIA, while IL10 (OR: 0.5, p < 0.002) was protective. sCD154 was elevated in all JIA subtypes, with highest levels among more severe subtypes. IL1β, IL6, IL8, sIL2R and TNFα were also elevated in several JIA subtypes.</p> <p>Conclusion</p> <p>Serum levels of sCD154, IL1β, IL6, IL8, sIL2R and TNFα are elevated in most JIA subtypes, suggesting a major role for sCD154, and these cytokines and cytokine receptors in the pathogenesis of JIA.</p

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe