Introduction: Lung Ultrasound is an accessible, low-cost technique that has demonstrated its usefulness in the prognostic stratification of COVID-19 patients. In addition, according to previous studies, it can guide us towards the potential aetiology, especially in epidemic situations such as the current one.
Patients And Methods: 40 patients were prospectively recruited, 30 with confirmed SARS-CoV-2 pneumonia and 10 with community-acquired pneumonia (CAP). The patients included underwent both a chest X-ray and ultrasound.
Results: There were no differences in the 2 groups in terms of clinical and laboratory characteristics. The main ultrasound findings in the SARS-CoV-2 group were the presence of confluent B lines and subpleural consolidations and hepatinization in the CAP group. Pleural effusion was more frequent in the CAP group. There were no normal lung ultrasound exams. Analysis of the area under the curve (AUC) curves showed an area under the curve for Lung Ultrasound of 89.2% (95% CI: 75%.0-100%, p < .001) in the identification of SARS-CoV-2 pneumonia. The cut-off value for the lung score of 10 had a sensitivity of 93.3% and a specificity of 80.0% (p < .001).
Discussion: The combination of the findings of the Lung Ultrasound, with a Lung Score greater than 10, added to the rest of the additional tests, can be an excellent tool to predict the aetiology of the pneumonia.
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http://dx.doi.org/10.1016/j.reumae.2021.09.006 | DOI Listing |
Ultrasound J
January 2025
Health Sciences North Research Institute, Sudbury, ON, Canada.
The duration of mechanical systole-also termed the flow time (FT) or left ventricular ejection time (LVET)-is measured by Doppler ultrasound and increasingly used as a stroke volume (SV) surrogate to guide patient care. Nevertheless, confusion exists as to the determinants of FT and a critical evaluation of this measure is needed. Using Doppler ultrasound of the left ventricular outflow tract velocity time integral (LVOT VTI) as well as strain and strain rate echocardiography as grounding principles, this brief commentary offers a model for the independent influences of FT.
View Article and Find Full Text PDFRadiol Cardiothorac Imaging
February 2025
From the Department of Biomedical Engineering (X.Z.) and Columbia Magnetic Resonance Research Center (CMRRC) (W.S.), Columbia University, New York, NY; Departments of Medicine (C.B.C., J.P.F.) and Radiology (J.P.F.), University of California at Los Angeles, Los Angeles, Calif; Department of Radiology, Weill Cornell Medicine, New York, NY (M.R.P.); Department of Radiology (M.R.P., S.M.D., S.J.), Department of Medicine (M.C.B., R.G.B.), Department of Epidemiology (R.G.B.), Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics (W.S.), and Institute of Human Nutrition (W.S.), Columbia University Irving Medical Center, 632 W 168th St, PH-17, New York, NY 10032; Department of Radiology (B.A.V., J.A.C.L.) and Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine (N.N.H.), Johns Hopkins University, Baltimore, Md; Department of Radiology, University of Michigan, Ann Arbor, Mich (P.P.A.); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wis (D.A.B.); Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC (D.C.); Departments of Radiology, Medicine, and the Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa (E.A.H.); Sections on Cardiology and Geriatrics, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (D.W.K.); Division of Pulmonary, Critical Care, Sleep, and Allergy (J.A.K.) and Department of Radiology, College of Medicine (M.G.M.), University of Illinois at Chicago, Chicago, Ill; Department of Radiology and Biomedical Imaging (Y.J.L., J.L.), Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, School of Medicine (P.G.W.), and Cardiovascular Research Institute (P.G.W.), University of California at San Francisco, San Francisco, Calif; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Wake Forest University, Winston-Salem, NC (J.O., S.P.P.); Division of Pulmonary Medicine, Department of Medicine, Mayo Clinic, Phoenix, Ariz (V.E.O.); Department of Medicine, University of Utah, Salt Lake City, Utah (R.P.); Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.S.); Department of Radiology, Hannover Medical School, Hannover, Germany (J.V.C.); and BREATH, Member of the German Center for Lung Research (DZL), Hannover, Germany (J.V.C.).
Purpose To assess the repeatability of real-time cine pulmonary MRI measures of metronome-paced tachypnea (MPT)-induced dynamic hyperinflation and its relationship with chronic obstructive pulmonary disease (COPD) severity. Materials and Methods SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) (ClinicalTrials.gov identifier no.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology and Research Institute of Radiology (Y.A., S.M.L., J.C., K.H.D., J.B.S.) and Department of Cardiothoracic Surgery (S.H.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea.
Background The ninth edition of the TNM classification for lung cancer revised the N2 categorization, improving patient stratification, but prognostic heterogeneity remains for the N1 category. Purpose To define the optimal size cutoff for a bulky lymph node (LN) on CT scans and to evaluate the prognostic value of bulky LN in the clinical N staging of lung cancer. Materials and Methods This retrospective study analyzed patients who underwent lobectomy or pneumonectomy for lung cancer between January 2013 and December 2021, divided into development (2016-2021) and validation (2013-2015) cohorts.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 (Y.Z., D.F.Y., C.I.H.); and Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China (Y.Z.).
Lung cancer is the leading cause of cancer deaths globally. In various trials, the ability of low-dose CT screening to diagnose early lung cancers leads to high cure rates. It is widely accepted that the potential benefits of low-dose CT screening for lung cancer outweigh the harms.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
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