Thrombospondins 1 and 2 (TSP-1/2) belong to a family of extracellular glycoproteins with angiostatic and synaptogenic properties. Although TSP-1/2 have been postulated to drive the resolution of postischemic angiogenesis, their role in synaptic and functional recovery is unknown. We investigated whether TSP-1/2 are necessary for synaptic and motor recovery after stroke. Focal ischemia was induced in 8- to 12-week-old wild-type (WT) and TSP-1/2 knockout (KO) mice by unilateral occlusion of the distal middle cerebral artery and the common carotid artery (CCA). Thrombospondins 1 and 2 increased after stroke, with both TSP-1 and TSP-2 colocalizing mostly to astrocytes. Wild-type and TSP-1/2 KO mice were compared in angiogenesis, synaptic density, axonal sprouting, infarct size, and functional recovery at different time points after stroke. Using the tongue protrusion test of motor function, we observed that TSP-1/2 KO mice exhibited significant deficit in their ability to recover function (P<0.05) compared with WT mice. No differences were found in infarct size and blood vessel density between the two groups after stroke. However, TSP-1/2 KO mice exhibited significant synaptic density and axonal sprouting deficits. Deficiency of TSP-1/2 leads to impaired recovery after stroke mainly due to the role of these proteins in synapse formation and axonal outgrowth.

Background: Management of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligence system that uses deep learning may improve radiology workflow for management of thyroid nodules.Purpose: To develop a deep learning algorithm that uses thyroid US images to decide whether a thyroid nodule should undergo a biopsy and to compare the performance of the algorithm with the performance of radiologists who adhere to American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Materials and Methods:In this retrospective analysis, studies in patients referred for US with subsequent fine-needle aspiration or with surgical histologic analysis used as the standard were evaluated. The study period was from August 2006 to May 2010. A multitask deep convolutional neural network was trained to provide biopsy recommendations for thyroid nodules on the basis of two orthogonal US images as the input. In the training phase, the deep learning algorithm was first evaluated by using 10-fold cross-validation. Internal validation was then performed on an independent set of 99 consecutive nodules. The sensitivity and specificity of the algorithm were compared with a consensus of three ACR TI-RADS committee experts and nine other radiologists, all of whom interpreted thyroid US images in clinical practice.Results: Included were 1377 thyroid nodules in 1230 patients with complete imaging data and conclusive cytologic or histologic diagnoses. For the 99 test nodules, the proposed deep learning algorithm achieved 13 of 15 (87%: 95% confidence interval [CI]: 67%, 100%) sensitivity, the same as expert consensus (P . .99) and higher than five of nine radiologists. The specificity of the deep learning algorithm was 44 of 84 (52%; 95% CI: 42%, 62%), which was similar to expert consensus (43 of 84; 51%; 95% CI: 41%, 62%; P = .91) and higher than seven of nine other radiologists. The mean sensitivity and specificity for the nine radiologists was 83% (95% CI: 64%, 98%) and 48% (95% CI: 37%, 59%), respectively. Conclusion:Sensitivity and specificity of a deep learning algorithm for thyroid nodule biopsy recommendations was similar to that of expert radiologists who used American College of Radiology Thyroid Imaging and Reporting Data System guidelines.

hyroid nodules are an extremely common finding at US and other imaging studies (1,2). Although most thyroid nodules are benign, many patients are subjected to a costly workup that may include one or more biopsies, follow-up imaging, and even diagnostic lobectomy (3). This contributes to the overdiagnosis of thyroid cancers that are not clinically significant (4). Over the past decade, multiple groups have developed biopsy guidelines for thyroid nodules based on their appearance at US, but some guidelines are difficult to apply and all lead to high false-positive rates (benign nodules for which biopsy is recommended). With these issues in mind, a committee of the American College of Radiology (ACR) created the Thyroid Imaging Reporting and Data System (TI-RADS) to determine if thyroid nodules depicted at US require biopsy or follow-up (5). Nodules are awarded points based on features in five categories-composition, echogenicity, shape, margin, and echogenic foci. The more suspicious the feature, the higher its point value. Points are summed to categorize a nodule into one of five TI-RADS risk levels, TR1 to TR5 (Table 1). Management recommendations are determined by using the risk level and the maximum size of the nodule. The points assigned to each feature in ACR TI-RADS were based on evidence in the literature and expert consensus. Therefore, it is possible that the performance of the system could be improved by optimization of the points assigned to each US feature. Given the problem of overdiagnosis in thyroid imaging, this might improve specificity without sacrificing sensitivity. Indeed, the ACR TI-RADS committee recognized that certain features may warrant higher or lower point values to achieve optimal performance (5).

In an analysis of patients with NAFLD or NASH, we determined that although The MRI-PDFF correlated with steatosis grade and NAS, and inversely with fibrosis stage, it was suboptimal in identification of patients with NAS >4 or advanced fibrosis. Although MRI-PDFF is an important imaging biomarker for continued evaluation of this patient population, liver biopsy analysis is still necessary.

Structured reporting of CTE for IBD improved documentation of key reporting features for trainees and faculty, though there was minimal impact on accuracy. Referring physicians subjectively preferred the structured reports.

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