ArticleInternational journal of medical informatics2021
An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.
Article in International journal of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- CT-based radiomics nomogram for distinguishingTranslational pediatrics · 2026Article
- Article
- The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model.BMC pulmonary medicine · 2024Article
- Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease.Journal of thoracic disease · 2024Article
- Exploring the Synergistic Potential of Radiomics and Laboratory Biomarkers for Enhanced Identification of Vulnerable COVID-19 Patients.Microorganisms · 2023Article
- Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2023Article
- Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients.Frontiers in oncology · 2023Article
- An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.Medical physics · 2022Article
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7 authors.
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Abstract
backgroundThis study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia.
methodsCOVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram.
resultsTwo clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve
conclusionsThe radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.
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