Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans.
In: BMC medical imaging, Jg. 21 (2021-08-12), Heft 1, S. 123
Online
academicJournal
Zugriff:
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. Trial registration Retrospectively registered.
(© 2021. The Author(s).)
Titel: |
Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans.
|
---|---|
Autor/in / Beteiligte Person: | Mahmoudi, S ; Martin, SS ; Ackermann, J ; Zhdanovich, Y ; Koch, I ; Vogl, TJ ; Albrecht, MH ; Lenga, L ; Bernatz, S |
Link: | |
Zeitschrift: | BMC medical imaging, Jg. 21 (2021-08-12), Heft 1, S. 123 |
Veröffentlichung: | London : BioMed Central, [2001-, 2021 |
Medientyp: | academicJournal |
ISSN: | 1471-2342 (electronic) |
DOI: | 10.1186/s12880-021-00654-9 |
Schlagwort: |
|
Sonstiges: |
|