ArticleEuropean journal of radiology2021
Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.
Article in European journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 30 citations in OpenAlex.
- A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.AJNR. American journal of neuroradiology · 2024Article
- Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection.Journal of clinical medicine · 2023Article
- Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.European radiology experimental · 2023Article
- Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.Tomography (Ann Arbor, Mich.) · 2022Article
- Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia.BMC medical imaging · 2022Article
- Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach.Physical and engineering sciences in medicine · 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
- A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.Medical physics · 2022Article
- Evaluation of the dependence of radiomic features on the machine learning model.Insights into imaging · 2022Article
- Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.European journal of radiology open · 2022Review
- Artificial intelligence-driven assessment of radiological images for COVID-19.Computers in biology and medicine · 2021Review
- Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.Scientific reports · 2021Article
- A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia.Diagnostics (Basel, Switzerland) · 2021Article
- A practical integrated radiomics model predicting intensive care hospitalization in COVID-19.Critical care (London, England) · 2021Article
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Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19.
methodsBetween 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists. The latest version of Pyradiomics was used for feature extraction. Four classifiers (linear classifier, k-nearest neighbour, least absolute shrinkage and selection operator [LASSO], and random forest) were used to differentiate SARS-CoV-2 positive and SARS-CoV-2 negative patients. Comparison of the performance of the classifiers and radiologists was evaluated by ROC curve and Kappa score.
resultsWe manually segmented 16,053 CT slices, comprising 32,625 pneumonia lesions, from the CT scans of all patients. Using Pyradiomics, 120 radiomic features were extracted from each image. The key radiomic features screened by different classifiers varied and lead to significant differences in classification accuracy. The LASSO achieved the best performance (sensitivity: 72.2%, specificity: 75.1%, and AUC: 0.81) on the external validation dataset and attained excellent agreement (Kappa score: 0.89) with radiologists (average sensitivity: 75.6%, specificity: 78.2%, and AUC: 0.81). All classifiers indicated that "Original_Firstorder_RootMeanSquared" and "Original_Firstorder_Uniformity" were significant features for this task.
conclusionsWe identified radiomic features that were significantly associated with the classification of COVID-19 pneumonia using multiple classifiers. The quantifiable interpretation of the differences in features between the two groups extends our understanding of CT imaging characteristics of COVID-19 pneumonia.
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