1. Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans
- Author
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Iris Q. Grunwald, Julija Vlahovic, Maria Politi, Shrey Mathur, Silke Walter, Rafael Namias, Panagiotis Papanagiotou, George Harston, Olivier Joly, Stephen Gerry, Marco Essig, Anna Podlasek, Khawar Hussain, Klaus Fassbender, Viola Wagner, Johann Kulikovski, Sweni Shah, and Wolfgang Reith
- Subjects
Middle Cerebral Artery ,medicine.medical_specialty ,Computed Tomography Angiography ,Collateral ,Intraclass correlation ,Clinical Decision-Making ,Collateral Circulation ,030204 cardiovascular system & hematology ,Spearman's rank correlation coefficient ,Machine Learning ,Automation ,03 medical and health sciences ,0302 clinical medicine ,Predictive Value of Tests ,medicine ,Humans ,cardiovascular diseases ,Stroke ,Thrombectomy ,Acute stroke ,Computed tomography angiography ,Original Paper ,medicine.diagnostic_test ,business.industry ,Patient Selection ,Prognosis ,Collateral circulation ,medicine.disease ,Triage ,Cerebral Angiography ,Neurology ,Cerebrovascular Circulation ,Radiographic Image Interpretation, Computer-Assisted ,Neurology (clinical) ,Radiology ,Cardiology and Cardiovascular Medicine ,business ,Blood Flow Velocity ,030217 neurology & neurosurgery - Abstract
Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this study, we used a machine learning approach to categorise the degree of collateral flow in 98 patients who were eligible for mechanical thrombectomy and generate an e-CTA collateral score (CTA-CS) for each patient (e-STROKE SUITE, Brainomix Ltd., Oxford, UK). Three experienced neuroradiologists (NRs) independently estimated the CTA-CS, first without and then with knowledge of the e-CTA output, before finally agreeing on a consensus score. Addition of the e-CTA improved the intraclass correlation coefficient (ICC) between NRs from 0.58 (0.46–0.67) to 0.77 (0.66–0.85, p = 0.003). Automated e-CTA, without NR input, agreed with the consensus score in 90% of scans with the remaining 10% within 1 point of the consensus (ICC 0.93, 0.90–0.95). Sensitivity and specificity for identifying favourable collateral flow (collateral score 2–3) were 0.99 (0.93–1.00) and 0.94 (0.70–1.00), respectively. e-CTA correlated with the Alberta Stroke Programme Early CT Score (Spearman correlation 0.46, p < 0.001) highlighting the value of good collateral flow in maintaining tissue viability prior to reperfusion. In conclusion, e-CTA provides a real-time and fully automated approach to collateral scoring with the potential to improve consistency of image interpretation and to independently quantify collateral scores even without expert rater input.
- Published
- 2019