ArticleComputers in biology and medicine2022
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.European radiology · 2024Pooled it
- Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study.BMC pulmonary medicine · 2024Trial
- Development and validation of a clinical-radiomics nomogram for differentiatingFrontiers in pediatrics · 2026Article
- CT-based radiomics models for predicting the prognosis of children with mycoplasma pneumonia.Frontiers in cellular and infection microbiology · 2026Article
- Predicting Short-Term Outcome of COVID-19 Pneumonia Using Deep Learning-Based Automatic Detection Algorithm Analysis of Serial Chest Radiographs.Bioengineering (Basel, Switzerland) · 2025Article
- Development and multicentric external validation of a prognostic COVID-19 severity model based on thoracic CT.BMC medical informatics and decision making · 2025Article
- Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics.Bioengineering (Basel, Switzerland) · 2025Article
- Severity assessment of COVID-19 disease: radiological visual score versus automated quantitative CT parameters using a pneumonia analysis algorithm.Frontiers in medicine · 2025Article
- Self-reported checklists and quality scoring tools in radiomics: a meta-research.European radiology · 2024Article
- Machine learning-based analysis ofPhysical and engineering sciences in medicine · 2024Article
- Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics.Radiological physics and technology · 2024Article
- Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach.Life (Basel, Switzerland) · 2024Article
- Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection.Journal of clinical medicine · 2023Article
- Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment.Diagnostics (Basel, Switzerland) · 2023Review
- Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study.Journal of digital imaging · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study.Frontiers in medicine · 2023Article
- The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study.Frontiers in medicine · 2023Article
- Immunohematologic Biomarkers in COVID-19: Insights into Pathogenesis, Prognosis, and Prevention.Pathogens & immunity · 2023Review
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Authors and funding
49 authors.
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Abstract
backgroundWe aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.
methodsWhole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.
resultsIn the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.
conclusionLung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.
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