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| Verfasst von: | Gerhards, Catharina [VerfasserIn]  |
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| | Haselmann, Verena [VerfasserIn] |
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| | Schaible, Samuel F. [VerfasserIn] |
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| | Ast, Volker [VerfasserIn] |
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| | Kittel, Maximilian [VerfasserIn] |
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| | Thiel, Manfred [VerfasserIn] |
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| | Hertel, Alexander [VerfasserIn] |
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| | Schönberg, Stefan [VerfasserIn] |
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| | Neumaier, Michael [VerfasserIn] |
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| | Froelich, Matthias F. [VerfasserIn] |
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| Titel: | Exploring the synergistic potential of radiomics and laboratory biomarkers for enhanced identification of vulnerable COVID-19 patients |
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| Verf.angabe: | Catharina Gerhards, Verena Haselmann, Samuel F. Schaible, Volker Ast, Maximilian Kittel, Manfred Thiel, Alexander Hertel, Stefan O. Schoenberg, Michael Neumaier and Matthias F. Froelich |
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| Jahr: | 2023 |
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| Umfang: | 18 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Veröffentlicht: 3. Juli 2023 ; Gesehen am 24.08.2023 |
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| Titel Quelle: | Enthalten in: Microorganisms |
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| Ort Quelle: | Basel : MDPI, 2013 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 11(2023), 7, Artikel-ID 1740, Seite 1-18 |
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| ISSN Quelle: | 2076-2607 |
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| Abstract: | Background: Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT. Methods: Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting. Results: The adapted integrated model classifying patients into “ICU/no ICU demand” comprises six radiomics and seven laboratory biomarkers. The models’ accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91. Conclusion: The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients. |
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| DOI: | doi:10.3390/microorganisms11071740 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.3390/microorganisms11071740 |
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| | kostenfrei: Volltext: https://www.mdpi.com/2076-2607/11/7/1740 |
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| | DOI: https://doi.org/10.3390/microorganisms11071740 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | algorithms |
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| | cell-free nucleic acid |
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| | coronavirus infection |
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| | COVID-19 |
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| | integrative medicine |
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| | intensive care units |
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| | SARS-CoV-2 |
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| | thoracic radiography |
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| K10plus-PPN: | 1857865278 |
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| Verknüpfungen: | → Zeitschrift |
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Exploring the synergistic potential of radiomics and laboratory biomarkers for enhanced identification of vulnerable COVID-19 patients / Gerhards, Catharina [VerfasserIn]; 2023 (Online-Ressource)
