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 ϭ 0.85) and was significantly better than any single feature such as thrombus length (P Ͻ .001), volume (P Ͻ .001), and permeability as measured by mean attenuation increase (P Ͻ .001), maximum attenuation in CTA (P Ͻ .001), maximum attenuation increase (P Ͻ .001), and assessment of residual flow grade (P Ͻ .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. ABBREVIATIONS: AUC ϭ area under the curve; GLCM ϭ gray-level co-occurrence matrix; LSW ϭ level-spot waves; ROC ϭ receiver operating characteristic
Background Manual segmentations of intracranial hemorrhage on non-contrast CT images are the gold-standard in measuring hematoma growth but are prone to rater variability. Aims We demonstrate that a convex optimization-based interactive segmentation approach can accurately and reliably measure intracranial hemorrhage growth. Methods Baseline and 16-h follow-up head non-contrast CT images of 46 subjects presenting with intracranial hemorrhage were selected randomly from the ANNEXA-4 trial imaging database. Three users semi-automatically segmented intracranial hemorrhage to measure hematoma volume for each timepoint using our proposed method. Segmentation accuracy was quantitatively evaluated compared to manual segmentations by using Dice similarity coefficient, Pearson correlation, and Bland–Altman analysis. Intra- and inter-rater reliability of the Dice similarity coefficient and intracranial hemorrhage volumes and volume change were assessed by the intraclass correlation coefficient and minimum detectable change. Results Among the three users, the mean Dice similarity coefficient, Pearson correlation, and mean difference ranged from 76.79% to 79.76%, 0.970 to 0.980 ( p < 0.001), and −1.5 to −0.4 ml, respectively, for all intracranial hemorrhage segmentations. Inter-rater intraclass correlation coefficients between the three users for Dice similarity coefficient and intracranial hemorrhage volume were 0.846 and 0.962, respectively, and the corresponding minimum detectable change was 2.51 ml. Inter-rater intraclass correlation coefficient for intracranial hemorrhage volume change ranged from 0.915 to 0.958 for each user compared to manual measurements, resulting in an minimum detectable change range of 2.14 to 4.26 ml. Conclusions We spatially and volumetrically validate a novel interactive segmentation method for delineating intracranial hemorrhage on head non-contrast CT images. Good spatial overlap, excellent volume correlation, and good repeatability suggest its usefulness for measuring intracranial hemorrhage volume and volume change on non-contrast CT images.
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