Potential of High Dimensional Radiomic Features to Assess Flowing Blood Components in Non-contrast CT Scans

Abstract Background: To assess the potential of radiomic features to quantify components of flowing blood 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 ± 24h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest with consecutive radiomic analysis. 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 inherit the potential to augment the data in our exemplary use case of flowing blood component assessment.Trial registration: Retrospectively registered.