ArticleRadiology of infectious diseases (Beijing, China)2020
Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia.
Article in Radiology of infectious diseases (Beijing, China), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A prediction model for bronchoalveolar lavage in children with Mycoplasma pneumoniae pneumonia and consolidation.Scientific reports · 2026Article
- CT-based radiomics models for predicting the prognosis of children with mycoplasma pneumonia.Frontiers in cellular and infection microbiology · 2026Article
- Distinguishing nontuberculous mycobacterial lung disease from pulmonary tuberculosis using radiomics machine learning models from CT images.Frontiers in medicine · 2026Article
- Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis from COVID-19 Pneumonia Using a CT-based Radiomics Nomogram.Current medical imaging · 2025Article
- Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm.Journal of clinical medicine · 2024Article
- Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.Frontiers in medicine · 2024Article
- A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days.Frontiers in medicine · 2024Article
- Analysis of CT signs, radiomic features and clinical characteristics for delta variant COVID-19 patients with different vaccination status.BMC medical imaging · 2022Article
- Increased Serum Mir-150-3p Expression Is Associated with Radiological Lung Injury Improvement in Patients with COVID-19.Viruses · 2022Article
- Steroids Therapy in Patients With Severe COVID-19: Association With Decreasing of Pneumonia Fibrotic Tissue Volume.Frontiers in medicine · 2022Article
- DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.Diagnostics (Basel, Switzerland) · 2021Article
- The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.Diagnostics (Basel, Switzerland) · 2021Review
- Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy.Biomedical signal processing and control · 2021Article
- Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial intelligence-based approaches for COVID-19 patient management.Intelligent medicine · 2021Review
- Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism.Journal of digital imaging · 2021Article
- Generalized chest CT and lab curves throughout the course of COVID-19.Scientific reports · 2021Article
- Management of SARS-CoV-2 pneumonia.Journal of medical virology · 2021Review
- The association between clinical laboratory data and chest CT findings explains disease severity in a large Italian cohort of COVID-19 patients.BMC infectious diseases · 2021Observational
- Intermittent High-Frequency Percussive Ventilation Therapy in 3 Patients with Severe COVID-19 Pneumonia.The American journal of case reports · 2021Article
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Authors and funding
9 authors.
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Abstract
objectiveTo quantify coronavirus diseases 2019 (COVID-19) pneumonia and to explore whether quantitative computer tomography (CT) could be used to assess severity on admission. MATERIALS AND
methodsFrom January 17 to February 9, 2020, 38 hospitalized patients with COVID-19 pneumonia were consecutively enrolled in our hospitals. All clinical data and the chest CT on admission were retrospectively reviewed and analyzed. Firstly, a quantitative method based on multi-scale convolutional neural networks was used to assess the infected lung segments and this was compared with the semi-quantitative method. Secondly, the quantitative method was tested with laboratory results and the pneumonia severity index (PSI) by correlation analyses. Thirdly, both quantitative and semi-quantitative parameters between patients with different PSI were compared.
resultsThirty cases were finally enrolled: 16 (53.33%) of them were male, and the mean age was 48 years old. The interval from onset symptoms to first chest CT scan was 8 days. The proportion of ground glass opacity (GGO), consolidation and the total lesion based on the quantitative method was positively correlated with the semi-quantitative CT score (
conclusionsQuantitative CT may have potential in assessing the severity of COVID-19 pneumonia on admission.
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