Epidemiologic methods on HCV exposure can be used to characterize the magnitude of exposures to HCV infection, target interventions to reduce exposures, and provide the best method for evaluating interventions by demonstrating the reduction of exposure to HCV infection.

BackgroundCarotid web (CaW) is a shelf-like linear filling defect in the posterior aspect of the internal carotid bulb, representing an intimal variant of fibromuscular dysplasia. The diagnosis of CaW is traditionally restricted to digital subtraction angiography (DSA), CT/MR angiography (CTA/MRA), and Duplex ultrasonography. In this series of patients with acute ischemic stroke, we evaluated the potential utility of intravascular ultrasound (IVUS) in further characterizing suspected CaWs.MethodsThis is a case series of three patients with suspected CaW who underwent DSA for treatment or investigation of large vessel occlusion strokes. In all cases the stroke investigation failed to identify an alternative cause, and the stroke etiology was attributed to a symptomatic CaW. The procedure consisted of positioning a guide catheter in the common carotid artery, navigating the IVUS probe distal to the carotid bulb, and then retracting the probe with a manual pullback. The acquired images were then reviewed in an independent workstationResultsIn two of the three cases, IVUS showed an isoechoic-to-hyperechoic focal eccentric area at the posterior carotid bulb, consistent with CaW. The endoluminal protrusion was inconspicuous on IVUS due to the low resolution of ultrasound not allowing a clear differentiation between fibrosis, thrombosis, and atherosclerosis. No abnormalities commonly associated with atherosclerotic disease or dissections were noted. The CaW could not be depicted in the third patient.ConclusionThe use of IVUS in the diagnosis of CaW may have limited relevance. Continued investigation of other imaging modalities for accurate CaW diagnosis is recommended.

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Recent advances in medical image analysis have been made to improve our understanding of how disease develops, behaves, and responds to treatment. Magnetic resonance imaging (MRI) and positron emission tomography (PET) advanced imaging strategies provide structural and functional phenotypic biomarkers that correlate with key disease processes. Through radiomics and radiogenomics, ML-medical imaging has opened up new perspectives in high-grade glioma diagnosis. As a result, non-invasive and in vivo biomarkers for patient survival, tumor recurrence, and genomics are identified. Tumor genomic imaging signatures can help identify patients who benefit from targeted therapies. Molecular characterization of gliomas and prediction of their evolution would allow treatment optimization. Radiomics-based biomarkers allow for a more in-depth analysis of pathophysiologic processes and insights into diagnosing better, classifying, stratifying, and prognosticating brain tumors and assessing their response to therapy. Radiomics is a new data-driven approach that can help answer clinical questions like diagnosis, prognosis, and treatment response. With encouraging outcomes in brain tumor patients, radiomics and deep learning are still not widely used in clinical practice, requiring more extensive and practical clinical studies.

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