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Published on: August 28, 2018
Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography
Lennard Wolff1, Simone M Uniken Venema2, Sven P R Luijten3
1Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands. l.wolff.1@erasmusmc.nl.
Insights
An AI algorithm for assessing collateral circulation in acute ischemic stroke patients performed similarly to expert radiologists. This automated collateral score aids in predicting patient outcomes and functional independence after endovascular treatment.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Collateral circulation is crucial for endovascular treatment outcomes in acute ischemic stroke.
- Accurate assessment of collateral status is vital for patient management.
Purpose of the Study:
- To evaluate the performance of a commercial AI algorithm for collateral scoring (CS) in acute ischemic stroke patients.
- To compare AI-driven CS with expert radiologist assessments.
Main Methods:
- Retrospective analysis of CT angiography (CTA) scans from the MR CLEAN Registry (n=1024).
- Comparison of visual CS (radiologists) with AI-quantified CS (0-100%).
- Assessment of Area Under the Curve (AUC) for discriminating good vs. poor collaterals and predicting functional independence.
Main Results:
- 59% agreement between visual and automated CS.
- AUC of 0.87 for AI in discriminating good vs. poor collaterals.
- AI-based CS showed similar predictive performance for functional independence (AUC 0.66) compared to visual CS (AUC 0.64).
Conclusions:
- Automated CS software performs comparably to expert radiologists in assessing collateral status.
- The AI tool effectively predicts functional independence in stroke patients.
- CTA acquisition timing did not impact the AI algorithm's performance.
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.
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