Background: Plain chest radiograph (CXR), although less sensitive than chest CT, is usually the first-line imaging modality used for patients with symptomatic SARS-CoV-2 infection. The relation between radiological changes in CXR and clinical severity of the disease in symptomatic patients with COVID 19 has not been fully studied and there is no scoring system for the severity of the lung involvement, using the plain CXR.
Aim Of The Study: Current COVID-19 radiological literature is dominated by CT and a detailed description CXR appearances in relation to the disease time course is lacking. We propose an easy scoring system (CO X-RADS) to describe the severity of chest involvement in symptomatic COVID 19 patients using CXR and to correlate the radiological changes with the clinical severity of the disease.
Patients And Methods: The clinical manifestations and CXR findings were recorded in 500 symptomatic COVID-19 positive patients who were admitted to Hamad Medical Corporation (HMC) COVID-19 designated facility Center from January to June 2020. The severity and outcome of the disease included: intensive care unit admission, need for oxygen therapy, mechanical ventilation. and mortality rate.
Results: Most of our symptomatic patients (86.8%) had mild and moderate clinical manifestations. The remaining 13.2% had severe manifestations, including: fever, persistent dry cough, shortness of breath, dyspnea, abdominal and generalized body pains. Based on our radiological scoring system (0 to 10) patients were distributed according to their CXR findings into different categories and according to our suggested (CO X-RADS) severity system into five categories (0 to IV). Patients with mild clinical manifestations showed low scoring in CXR (score 0 up to 4) and they represented 72% of our patients. Patients with moderately severe clinical manifestations showed mainly GGO (scoring 5 and 6) and represented about 14.8% of patients. Patients presented with severe clinical manifestations had obvious lung consolidations at the time of presentation with CXR scoring system ≥ 7 and represented about 13.2% of patients.
Conclusion: We proposed a simple CXR reporting scoring system (CO X-RADS) to categorize COVID-19 patients according to their radiological severity. This radiological score was correlated well with the clinical severity score of patients. We encourage other centers to test this scoring system in correlation with the clinical status of patients.
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http://dx.doi.org/10.23750/abm.v91i4.10664 | DOI Listing |
J Magn Reson Imaging
January 2025
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
Background: Differentiation of benign myxomas and malignant myxoid sarcomas can be difficult with an overlapping spectrum of morphologic MR findings.
Purpose: To assess the diagnostic utility of MRI radiomics in the differentiation of musculoskeletal myxomas and myxoid sarcomas.
Study Type: Retrospective.
J Diabetes
January 2025
Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing City, Jiangsu Province, China.
Background: Iron is one of the most important elements in brain that may has a direct impact on the stability of central nervous system. The current study devoted to explore the alterations of iron distribution across the whole brain in type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI).
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Sci Rep
January 2025
DIAPath, Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles (ULB), 6041, Gosselies, Belgium.
Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Purpose: The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system considering zero-shot learning capability of LLMs, integrated with a comprehensive database of PET reading reports, in improving reference to prior reports and decision making.
Methods: We developed a custom LLM framework with retrieval capabilities, leveraging a database of over 10 years of PET imaging reports from a single center.
Sci Rep
January 2025
Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China.
Identification and diagnosis of tobacco diseases are prerequisites for the scientific prevention and control of these ailments. To address the limitations of traditional methods, such as weak generalization and sensitivity to noise in segmenting tobacco leaf lesions, this study focused on four tobacco diseases: angular leaf spot, brown spot, wildfire disease, and frog eye disease. Building upon the Unet architecture, we developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet) by enhancing the feature extraction module and integrating attention mechanisms.
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