Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts
© 2024. The Author(s)..
This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Scientific reports - 14(2024), 1 vom: 25. März, Seite 7079 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Selvam, Minmini [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 27.03.2024 Date Revised 28.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-024-57899-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370176529 |
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| 520 | |a This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19 | ||
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| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Nodules | |
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| 700 | 1 | |a Sadanandan, Abjasree |e verfasserin |4 aut | |
| 700 | 1 | |a Anand, Vikas K |e verfasserin |4 aut | |
| 700 | 1 | |a Ramesh, Sidharth |e verfasserin |4 aut | |
| 700 | 1 | |a Murali, Arunan |e verfasserin |4 aut | |
| 700 | 1 | |a Krishnamurthi, Ganapathy |e verfasserin |4 aut | |
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