TY  - JOUR
A1  - Mahmoudi, Scherwin
A1  - Martin, Simon
A1  - Ackermann, Jörg
A1  - Zhdanovich, Yauheniya
A1  - Koch, Ina
A1  - Vogl, Thomas J.
A1  - Albrecht, Moritz Hans Ernst
A1  - Lenga, Lukas Fabian
A1  - Bernatz, Simon
T1  - Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans
T2  - BMC medical imaging
N2  - 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.
KW  - Radiomics
KW  - Blood
KW  - Anemia
KW  - Artificial intelligence
KW  - CT
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/62622
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-626224
SN  - 1471-2342
N1  - 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.
VL  - 21
IS  - art. 123
SP  - 1
EP  - 10
PB  - BioMed Central
CY  - London
ER  -