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2203. Automated Estimation of Ischemic Core Volume on Non–Contrast-Enhanced CT via Machine Learning
Authors * Denotes Presenting Author
  1. Iris Chen *; University of California, Los Angeles
  2. Brian Tsui; University of California, Los Angeles
  3. Joe Qiao; University of California, Los Angeles
  4. William Hsu; University of California, Los Angeles
  5. Jeffrey Saver; University of California, Los Angeles
  6. David Liebeskind; University of California, Los Angeles
  7. Kambiz Nael; University of California, Los Angeles
Objective:
Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine non-contrast CT (NCCT). We used a ML-trained algorithm to quantify ischemic core volume on NCCT and compared the results to concurrent diffusion MRI as the reference standard in patients with AIS.

Materials and Methods:
We analyzed consecutive anterior circulation AIS patients who had baseline (pretreatment) NCCT and MRI (DWI). Ischemic lesion volume was calculated on MRI-DWI using an automated software (Olea Medical SAS, La Ciotat, France). An automatic segmentation approach using a combination of traditional 3D graphics and statistical methods, and ML classification techniques (Brainomix, Oxford, United Kingdom) was used to identify ischemic core voxels on NCCT. Total ischemic core volumes on ML-NCCT and DWI-MR were quantitatively compared by Bland-Altman plots and Pearson correlation.

Results:
A total of 50 patients (27 female, 23 male, mean age 72.6 years) were included. Baseline imaging was performed within 173 ? 143 minutes (mean ? SD) from symptom onset. The mean time difference between MRI and NCCT was 72 min. The baseline NIHSS was 14, 8-21 (Median, IQR). Algorithm-segmented ischemic core volume detected on NCCT was median 12.7 mL, IQR 3.5-26.0 mL. Ischemic core volume on DWI MRI was median 8.8 mL, IQR 3.2–34.0 mL. ML-NCCT core volumes significantly correlated with DWI MRI core volumes, r=0.61, p<0.001. The mean difference between the ML-NCCT and DWI MRI core volumes was 12.4 mL, p=0.81. For the reperfusion treatment threshold of an ischemic core volume within 70 mL, while no patients would have been excluded using our algorithm, five patients would have been incorrectly dichotomized as having an ischemic volume of <70 mL compared to MRI.

Conclusion:
This ML-approach accurately quantifies ischemic core volume on NCCT compared to the reference standard of diffusion MRI in patients with AIS.