- Background: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. Methods: One hundred patients (median age, 69 years; range, 19–94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). Results: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). Conclusions: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment.
MetadatenVerfasserangaben: | Scherwin MahmoudiORCiDGND, Simon MartinORCiDGND, Jörg AckermannORCiDGND, Yauheniya Zhdanovich, Ina KochORCiD, Thomas J. VoglORCiDGND, Moritz Hans Ernst AlbrechtORCiDGND, Lukas Fabian LengaORCiDGND, Simon BernatzORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-626224 |
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DOI: | https://doi.org/10.1186/s12880-021-00654-9 |
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ISSN: | 1471-2342 |
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Titel des übergeordneten Werkes (Englisch): | BMC medical imaging |
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Verlag: | BioMed Central |
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Verlagsort: | London |
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Dokumentart: | Wissenschaftlicher Artikel |
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Sprache: | Englisch |
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Datum der Veröffentlichung (online): | 12.08.2021 |
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Datum der Erstveröffentlichung: | 12.08.2021 |
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Veröffentlichende Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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Datum der Freischaltung: | 02.11.2021 |
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Freies Schlagwort / Tag: | Anemia; Artificial intelligence; Blood; CT; Radiomics |
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Jahrgang: | 21 |
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Ausgabe / Heft: | art. 123 |
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Seitenzahl: | 10 |
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Erste Seite: | 1 |
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Letzte Seite: | 10 |
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Bemerkung: | The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Open Access funding enabled and organized by Projekt DEAL. |
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HeBIS-PPN: | 489040624 |
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Institute: | Medizin |
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DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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Sammlungen: | Universitätspublikationen |
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Lizenz (Deutsch): | Creative Commons - Namensnennung 4.0 |
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