Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans

  • 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.

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Author:Scherwin MahmoudiORCiDGND, Simon MartinORCiDGND, Jörg AckermannORCiDGND, Yauheniya Zhdanovich, Ina KochORCiD, Thomas J. VoglORCiDGND, Moritz Hans Ernst AlbrechtORCiDGND, Lukas Fabian LengaORCiDGND, Simon BernatzORCiDGND
Parent Title (English):BMC medical imaging
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Date of Publication (online):2021/08/12
Date of first Publication:2021/08/12
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2021/11/02
Tag:Anemia; Artificial intelligence; Blood; CT; Radiomics
Issue:art. 123
Page Number:10
First Page:1
Last Page:10
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.
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0