ArticleRadiology. Cardiothoracic imaging2020
CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia.
Article in Radiology. Cardiothoracic imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 58 citations in OpenAlex.
- Imaging biomarkers related to tumor-associated macrophage in immunotherapy treatment planning for non-small cell lung cancer.Translational lung cancer research · 2025Article
- Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung.Scientific reports · 2024Article
- Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.Journal of clinical medicine · 2024Article
- A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia.BMC medical imaging · 2024Article
- Radiomics analysis in medical imaging research.Journal of medical radiation sciences · 2023Article
- Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.Cancers · 2022Article
- Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.European radiology · 2022Article
- High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms.Scientific reports · 2022Article
- Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.Scientific reports · 2022Article
- Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?BMC medical imaging · 2022Article
- COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.Computers in biology and medicine · 2022Article
- A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients.BMC medical informatics and decision making · 2022Article
- Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study.BMC pulmonary medicine · 2022Article
- An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study.Frontiers in radiology · 2022Article
- Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.European radiology · 2022Article
- Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics.European journal of radiology open · 2022Article
- Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.International journal of clinical practice · 2022Article
- Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.The British journal of radiology · 2021Article
- An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.International journal of medical informatics · 2021Article
- Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT.Medical image analysis · 2021Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables. MATERIALS AND
methodsThis institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients,
resultsMost patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (
conclusionRadiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.
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