| Autor: |
Scherwin Mahmoudi, Simon S. Martin, Jörg Ackermann, Yauheniya Zhdanovich, Ina Koch, Thomas J. Vogl, Moritz H. Albrecht, Lukas Lenga, Simon Bernatz |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
BMC Medical Imaging, Vol 21, Iss 1, Pp 1-10 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2342 |
| DOI: |
10.1186/s12880-021-00654-9 |
| Popis: |
Abstract 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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink