Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography

Abstract

Objectives: Outcome of endovascular treatment in acute ischemic stroke patients depends on collateral circulation to provide blood supply to the ischemic territory. We evaluated the performance of a commercially available algorithm for assessing the collateral score (CS) in acute ischemic stroke patients.

Methods: Retrospectively, baseline CTA scans (≤ 3-mm slice thickness) with an intracranial carotid artery (ICA), middle cerebral artery segment M1 or M2 occlusion, from the MR CLEAN Registry (n = 1627) were evaluated. All CTA scans were evaluated for visual CS (0-3) by eight expert radiologists (reference standard). A Web-based AI algorithm quantified the collateral circulation (0-100%) for correctly detected occlusion sides. Agreement between visual CS and categorized automated CS (0: 0%, 1: > 0- ≤ 50%, 2: > 50- < 100%, 3: 100%) was assessed. Area under the curve (AUC) values for classifying patients in having good (CS: 2-3) versus poor (CS: 0-1) collaterals and for predicting functional independence (90-day modified Rankin Scale 0-2) were computed. Influence of CTA acquisition timing after contrast material administration was reported.

Results: In the analyzed scans (n = 1024), 59% agreement was found between visual CS and automated CS. An AUC of 0.87 (95% CI: 0.85-0.90) was found for discriminating good versus poor CS. Timing of CTA acquisition did not influence discriminatory performance. AUC for predicting functional independence was 0.66 (95% CI 0.62-0.69) for automated CS, similar to visual CS 0.64 (95% CI 0.61-0.68).

Conclusions: The automated CS performs similar to radiologists in determining a good versus poor collateral score and predicting functional independence in acute ischemic stroke patients with a large vessel occlusion.

Key points: • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in determining a good versus poor collateral score. • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in predicting functional independence in acute ischemic stroke patients with a large vessel occlusion. • The timing of computed tomography angiography acquisition after contrast material administration did not influence the performance of automated quantification of the collateral status.

Keywords: Algorithms; Collateral circulation; Ischemic stroke.

Fig. 1

Patient selection. *StrokeViewer exclusion criterion. EVT, endovascular treatment; CTA, computed tomography angiography; LVO, large vessel occlusion; ICA, internal carotid artery; ICA, internal carotid artery terminus; M1, M1 segment of the middle cerebral artery; M2, M2 segment of the middle cerebral artery; A1/A2, A1 or A2 segment of the anterior cerebral artery; M3/M4, M3 or M4 segment of the middle cerebral artery; MIP, maximum intensity projection; mm, millimeters

Fig. 2

Visual collateral score grading in patients with an M1 occlusion. 0: absent collaterals, 0% filling of the occluded territory. 1: poor collaterals, > 0% and ≤ 5 0% filling of the occluded territory. 2: moderate collaterals, > 50% and < 100% filling of the occluded territory. 3: good collaterals, 100% filling of the occluded territory

Fig. 3

Quantitative collateral score distribution for visual collateral score categories. Distribution of quantitative collateral scores per visual collateral score. Box plots with median, interquartile range, and 95% confidence intervals. Circle: one patient. Asterisk: four patients