Radiomics-Based Intracranial Thrombus Features on CT and CTA Predict Recanalization with Intravenous Alteplase in Patients with Acute Ischemic Stroke

Abstract

Background and purpose: Thrombus characteristics identified on non-contrast CT (NCCT) are potentially associated with recanalization with intravenous (IV) alteplase in patients with acute ischemic stroke (AIS). Our aim was to determine the best radiomics-based features of thrombus on NCCT and CT angiography associated with recanalization with IV alteplase in AIS patients and proximal intracranial thrombi.

Materials and methods: With a nested case-control design, 67 patients with ICA/M1 MCA segment thrombus treated with IV alteplase were included in this analysis. Three hundred twenty-six radiomics features were extracted from each thrombus on both NCCT and CTA images. Linear discriminative analysis was applied to select features most strongly associated with early recanalization with IV alteplase. These features were then used to train a linear support vector machine classifier. Ten times 5-fold cross-validation was used to evaluate the accuracy of the trained classifier and the stability of the selected features.

Results: Receiver operating characteristic curves showed that thrombus radiomics features are predictive of early recanalization with IV alteplase. The combination of radiomics features from NCCT, CTA, and radiomics changes is best associated with early recanalization with IV alteplase (area under the curve = 0.85) and was significantly better than any single feature such as thrombus length (P < .001), volume (P < .001), and permeability as measured by mean attenuation increase (P < .001), maximum attenuation in CTA (P < .001), maximum attenuation increase (P < .001), and assessment of residual flow grade (P < .001).

Conclusions: Thrombus radiomics features derived from NCCT and CTA are more predictive of recanalization with IV alteplase in patients with acute ischemic stroke with proximal occlusion than previously known thrombus imaging features such as length, volume, and permeability.

© 2019 by American Journal of Neuroradiology.

Figures

Fig 1.

Visualized feature images of thrombus of 2 patients, one with early recanalization and another without recanalization. The selected feature, Laws SD of LSW, is demonstrated. The LSW value (gray-level) of each pixel in the feature map is measuring the heterogeneity in the local region of each pixel.

Fig 2.

ROC results of the best radiomics features from different feature groups, compared with clinically relevant currently measurable features such as thrombus length, volume, and permeability measurements, as well as subjective assessment of the residual flow grade. The numbers in the parentheses following legend names denote AUC values and ranking after linear discriminative analysis.

Fig 3.

ROC curves for the prediction of early recanalization with IV alteplase using 67 patients (30 patients versus 37 controls). The bars on the curves represent the SDs of 10 repetitions. A, ROC curves for each feature group. B, ROC curves for different combinations of feature groups.