LI-RADS: A Conceptual and Historical Review from Its Beginning to Its Recent Integration into AASLD Clinical Practice Guidance

The Liver Imaging Reporting and Data System (LI-RADS®) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver observations in individuals at high risk for hepatocellular carcinoma (HCC). LI-RADS is supported and endorsed by the American College of Radiology (ACR). Upon its initial release in 2011, LI-RADS applied only to liver observations identified at CT or MRI. It has since been refined and expanded over multiple updates to now also address ultrasound-based surveillance, contrast-enhanced ultrasound for HCC diagnosis, and CT/MRI for assessing treatment response after locoregional therapy. The LI-RADS 2018 version was integrated into the HCC diagnosis, staging, and management practice guidance of the American Association for the Study of Liver Diseases (AASLD). This article reviews the major LI-RADS updates since its 2011 inception and provides an overview of the currently published LI-RADS algorithms

A multicenter assessment of interreader reliability of LI-RADS version 2018 for MRI and CT

Background: Various limitations have impacted research evaluating reader agreement for Liver Imaging-Reporting and Data System (LI-RADS). Purpose: To assess reader agreement of LI-RADS in an international multi-center, multireader setting using scrollable images. Materials and Methods: This retrospective study used de-identified clinical multiphase CT and MRI examinations and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted. Examination dates were October 2017 – August 2018 at the coordinating center. One untreated observation per examination was randomly selected using observation identifiers, and its clinically assigned features were extracted from the report. The corresponding LI-RADS v2018 category was computed as a re-scored clinical read. Each examination was randomly assigned to two of 43 research readers who independently scored the observation. Agreement for an ordinal modified four-category LI-RADS scale (LR-1/2, LR-3, LR-4, LR-5/M/tumor in vein) was computed using intra-class correlation coefficients (ICC). Agreement was also computed for dichotomized malignancy (LR-4/LR5/LR-M/LR-tumor in vein), LR-5, and LR-M. Agreement was compared between researchversus-research reads and research-versus-clinical reads. Results: 484 patients (mean age, 62 years ±10 [SD]; 156 women; 93 CT, 391 MRI) were included. ICCs for ordinal LI-RADS, dichotomized malignancy, LR-5, and LR-M were 0.68 (95% CI: 0.62, 0.74), 0.63 (95% CI: 0.56, 0.71), 0.58 (95% CI: 0.50, 0.66), and 0.46 (95% CI: 0.31, 0.61) respectively. Research-versus-research reader agreement was higher than research-versus-clinical agreement for modified four-category LI-RADS (ICC, 0.68 vs. 0.62, P = .03) and for dichotomized malignancy (ICC, 0.63 vs. 0.53, P = .005), but not for LR-5 (P = .14) or LR-M (P = .94). Conclusion: There was moderate agreement for Liver Imaging-Reporting and Data System v2018 overall. For some comparisons, research-versus-research reader agreement was higher than research-versus-clinical reader agreement, indicating differences between the clinical and research environments that warrant further study

Nanostructural Diversity of Synapses in the Mammalian Spinal Cord

This work for funded by the Biotechnology and Biological Sciences Research Council (BBSRC; BB/M021793/1), RS MacDonald Charitable Trust, Motor Neurone Disease (MND) Association UK (Miles/Apr18/863-791), the Engineering and Physical Sciences Research Council (EPSRC; EP/P030017/1), Welcome Trust (202932/Z/16/Z), European Research Council (ERC; 695568) and the Simons Initiative for the Developing Brain.Functionally distinct synapses exhibit diverse and complex organisation at molecular and nanoscale levels. Synaptic diversity may be dependent on developmental stage, anatomical locus and the neural circuit within which synapses reside. Furthermore, astrocytes, which align with pre and post-synaptic structures to form “tripartite synapses”, can modulate neural circuits and impact on synaptic organisation. In this study, we aimed to determine which factors impact the diversity of excitatory synapses throughout the lumbar spinal cord. We used PSD95-eGFP mice, to visualise excitatory postsynaptic densities (PSDs) using high-resolution and super-resolution microscopy. We reveal a detailed and quantitative map of the features of excitatory synapses in the lumbar spinal cord, detailing synaptic diversity that is dependent on developmental stage, anatomical region and whether associated with VGLUT1 or VGLUT2 terminals. We report that PSDs are nanostructurally distinct between spinal laminae and across age groups. PSDs receiving VGLUT1 inputs also show enhanced nanostructural complexity compared with those receiving VGLUT2 inputs, suggesting pathway-specific diversity. Finally, we show that PSDs exhibit greater nanostructural complexity when part of tripartite synapses, and we provide evidence that astrocytic activation enhances PSD95 expression. Taken together, these results provide novel insights into the regulation and diversification of synapses across functionally distinct spinal regions and advance our general understanding of the ‘rules’ governing synaptic nanostructural organisation.Publisher PDFPeer reviewe

Both Intrinsic Substrate Preference and Network Context Contribute to Substrate Selection of Classical Tyrosine Phosphatases.

Reversible tyrosine phosphorylation is a widespread posttranslational modification mechanism underlying cell physiology. Thus, understanding the mechanisms responsible for substrate selection by kinases and phosphatases is central to our ability to model signal transduction at a system level. Classical protein-tyrosine phosphatases can exhibit substrate specificity in vivo by combining intrinsic enzymatic specificity with the network of protein-protein interactions, which positions the enzymes in close proximity to their substrates. Here we use a high throughput approach, based on high density phosphopeptide chips, to determine the in vitro substrate preference of 16 members of the protein-tyrosine phosphatase family. This approach helped identify one residue in the substrate binding pocket of the phosphatase domain that confers specificity for phosphopeptides in a specific sequence context. We also present a Bayesian model that combines intrinsic enzymatic specificity and interaction information in the context of the human protein interaction network to infer new phosphatase substrates at the proteome level