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| http://dx.doi.org/10.3389/fcell.2022.1095235 | DOI Listing |
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Dual-layer dual-energy CT characterization of thrombus composition in acute pulmonary embolism and chronic thromboembolic pulmonary hypertension.
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Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
To evaluate dual-layer dual-energy computed tomography (dlDECT)-based characterization of thrombus composition for differentiation of acute pulmonary embolism (PE) and chronic thromboembolic pulmonary hypertension (CTEPH). This retrospective single center cohort study included 49 patients with acute PE and 33 patients with CTEPH who underwent CT pulmonary angiography on a dlDECT from 06/2016 to 06/2022. Conventional images), material specific images (virtual non-contrast [VNC], iodine density overlay [IDO], electron density [ED]), and virtual monoenergetic images (VMI) were analyzed.
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Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg/Havel, Brandenburg an der Havel, Germany.
The evolution of orthopaedics has witnessed a pronounced shift from generalist practice to subspecialisation, a trend that has fundamentally reshaped the field over the past century. Originally, orthopaedic surgeons were trained to address a broad spectrum of musculoskeletal conditions. However, increasing complexity and specialization in knowledge have driven a progressive narrowing of focus within orthopaedics.
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Radiotherapy Department, Montpellier Regional Cancer Institute, Montpellier, France.
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U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, United States of America.
Objective: With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).
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