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

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

SUBMITTER: Mahmoudi S 

PROVIDER: S-EPMC8359593 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature