ArticleKorean journal of radiology2020
CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19.
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 49 papers, 2 of them syntheses that pooled it.
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49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis.Journal of medical imaging and radiation sciences · 2023Pooled it
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Correlation Between Disease Severity and Chest Computed Tomography Findings in Coronavirus Disease 2019 Patients in a University Hospital of Bangladesh-An Observational Study.Health science reports · 2026Article
- Consolidation-to-ground-glass opacity ratio on chest CT as a prognostic marker for critical outcomes in COVID-19: a retrospective cohort study.BMC pulmonary medicine · 2025Article
- Semi-quantitative software evaluation of COVID-19 CT examinations-correlation with clinical parameters.Journal of thoracic disease · 2025Article
- Diagnostic Accuracy of Lung Ultrasound in Rabbit Subclinical Lung Lesions.Veterinary sciences · 2025Article
- Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators.Scientific data · 2025Article
- Novel tools for evaluating COVID-19 at the emergency department: Surfactant protein D level and CHARISMA score.Heliyon · 2024Article
- Article
- Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19.Kidney diseases (Basel, Switzerland) · 2024Article
- Article
- Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan.Respiratory research · 2023Article
- Neurological complications and effects of COVID-19: Symptoms and conceivable mechanisms.Brain hemorrhages · 2023Review
- Machine learning with multimodal data for COVID-19.Heliyon · 2023Review
- 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.Journal of digital imaging · 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
- The Risk Factors and Outcomes for Radiological Abnormalities in Early Convalescence of COVID-19 Patients Caused by the SARS-CoV-2 Omicron Variant: A Retrospective, Multicenter Follow-up Study.Journal of Korean medical science · 2023Article
- Coexistence of anti-MDA5 and anti-PL-7 in a patient with dermatomyositis: A case report.Clinical case reports · 2023Article
- Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.Medical image analysis · 2022Article
- The prognostic value of biomarker levels and chest imaging in patients with COVID-19 presenting to the emergency department.The American journal of emergency medicine · 2022Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). MATERIALS AND
methodsA total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19.
resultsPatients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (
conclusionCT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.
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