Objective: To investigate the low-dose chest computed tomography (CT) presentation and dynamic changes in patients with novel coronavirus disease 2019 (COVID-19) to improve understanding of this highly infectious disease.
Methods: The clinical and CT data of 16 patients with COVID-19 were retrospectively analyzed. Dynamic CTs were performed continuously after admission.
Results: Of the patients, 14 were moderate cases, and 2 were severe. Twelve patients underwent CT at the early onset stage. Single nodules or ground-glass opacities (GGOs) were found in 2 patients and multiple bilateral pulmonary lesions in 8 (consolidation-like opacities with or without small nodules in five and large GGOs with interlobular septal thickening in three). Ten had lesion growth and enlargement on the second CT. Fourteen patients underwent CT during the progressive stage, which revealed GGOs and focal consolidation in 6 of them, lung consolidation opacities in 5, and simple, large GGOs with interlobular septal thickening in 3. In both severe cases, the lesions continued to enlarge and grow, and the extent of consolidation continued to expand.
Conclusion: Low-dose chest CT can clearly reflect the morphology, density, and extent of COVID-19 nodules, and is beneficial for observing dynamic nodule changes and disease screening and monitoring.
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http://dx.doi.org/10.1016/j.jrid.2020.08.001 | DOI Listing |
Phys Med Biol
January 2025
Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, California, 90095, UNITED STATES.
Objective: The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.
Approach: A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel).
BMJ Open
December 2024
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Introduction: Early lung cancer screening (LCS) through low-dose CT (LDCT) is crucial but underused due to various barriers, including incomplete or inaccurate patient smoking data in the electronic health record and limited time for shared decision-making. The objective of this trial is to investigate a patient-centred intervention, MyLungHealth, delivered through the patient portal. The intervention is designed to improve LCS rates through increased identification of eligible patients and informed decision-making.
View Article and Find Full Text PDFChest
January 2025
Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London.
Background: Delirium is a common and serious syndrome of acute brain dysfunction associated with negative outcomes. Melatonin may have a role in delirium prevention for critically ill adults based on data from non-critically ill patient populations. Our objective was to assess the feasibility of a multi-centre, randomized, placebo-controlled trial testing the hypothesis that low-dose melatonin prevents delirium in critically ill adults.
View Article and Find Full Text PDFJ Thorac Oncol
January 2025
Department of General Internal Medicine and Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Hypothesis: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed-tomography (LDCT).
Methods: We developed a comorbidity index (PLCO-ci) using LCS-eligible participants' data from the Prostate Lung Colorectal and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts 5-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated by the area under the ROC curve (ROC).
Insights Imaging
January 2025
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Objectives: To investigate the image quality and diagnostic performance with ultra-low dose dual-layer detector spectral CT (DLSCT) by various reconstruction techniques for evaluation of pulmonary nodules.
Materials And Methods: Between April 2023 and December 2023, patients with suspected pulmonary nodules were prospectively enrolled and underwent regular-dose chest CT (RDCT; 120 kVp/automatic tube current) and ultra-low dose CT (ULDCT; 100 kVp/10 mAs) on a DLSCT scanner. ULDCT was reconstructed with hybrid iterative reconstruction (HIR), electron density map (EDM), and virtual monoenergetic images at 40 keV and 70 keV.
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