The British journal of radiology
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Comparative Study
Comparative effectiveness of 18F-FDG PET-CT and contrast-enhanced CT in the diagnosis of suspected large-vessel vasculitis.
Large-vessel vasculitis (LVV) is a serious illness with potentially life-threatening consequences. (18Fluorine) fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) has emerged as a valuable diagnostic tool in suspected LVV, combining the strengths of functional and structural imaging. This study aimed to compare the accuracy of FDG PET-CT and contrast-enhanced CT (CECT) in the evaluation of patients with LVV. ⋯ FDG PET-CT demonstrated excellent accuracy whilst CECT mural thickening showed good accuracy in the diagnosis of LVV. Both parameters showed a highly significant correlation. In hospitals without access to FDG PET-CT or in patients unsuitable for PET-CT (e.g. uncontrolled diabetes) CECT offers a viable alternative for the assessment of LVV. Advances in knowledge: FDG PET-CT is a highly accurate test for the diagnosis of LVV. Aorta:liver SUVmax ratio is the most specific parameter for LVV. In hospitals without PET-CT or in unsuitable patients e.g. diabetics, CECT is a viable alternative.
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Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. ⋯ This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.
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To investigate a fully automated CT-based adiposity tool, applying it to a longitudinal adult screening cohort. ⋯ This robust, fully automated CT adiposity tool allows for both individualized and population-based assessment of visceral and subcutaneous abdominal fat. Such data could be automatically derived at abdominal CT regardless of the study indication, potentially allowing for opportunistic cardiovascular risk stratification. Advances in knowledge: The CT-based adiposity tool described herein allows for fully automated measurement of visceral and subcutaneous abdominal fat, which can be used for assessing cardiovascular risk, metabolic syndrome, and for change over time.