ArticleKorean journal of radiology2020
Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score.
Article in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Quantification and Analysis of Lung Involvement by Artificial Intelligence in Patients with Progressive Pulmonary Fibrosis Treated with Nintedanib.Medicina (Kaunas, Lithuania) · 2025Article
- Article
- Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease.Journal of thoracic disease · 2024Article
- Semi-quantitative CT score reflecting the degree of pulmonary infection as a risk factor of hypokalemia in COVID-19 patients: a cross-sectional study.Frontiers in medicine · 2024Article
- The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia.Journal of clinical medicine · 2023Article
- Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.Journal of thoracic disease · 2023Article
- Challenges in the Management of Post-COVID-19 Pulmonary Fibrosis for the Latin American Population.Journal of personalized medicine · 2022Article
- Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.Heliyon · 2022Article
- Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2022Article
- Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.Tomography (Ann Arbor, Mich.) · 2022Article
- Steroids Therapy in Patients With Severe COVID-19: Association With Decreasing of Pneumonia Fibrotic Tissue Volume.Frontiers in medicine · 2022Article
- Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.medRxiv : the preprint server for health sciences · 2021Article
- Review on radiological evolution of COVID-19 pneumonia using computed tomography.World journal of radiology · 2021Article
- Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.Annals of translational medicine · 2021Article
- Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.Korean journal of radiology · 2021Article
- Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.Korean journal of radiology · 2021Article
- COVID-19 Imaging: What We Know Now and What Remains Unknown.Radiology · 2021Review
- Artificial intelligence-based approaches for COVID-19 patient management.Intelligent medicine · 2021Review
- Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds.Diagnostics (Basel, Switzerland) · 2021Article
- Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.Journal of thoracic disease · 2021Article
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8 authors.
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Abstract
objectiveTo compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19). MATERIALS AND
methodsWe retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods.
resultsThere were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters (
conclusionThe classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.
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