ArticleEuropean radiology2022
Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Poststroke Outcomes.AJNR. American journal of neuroradiology · 2026Article
- Time series-derived fractal dimension of CT perfusion in acute ischemic stroke: a promising marker for hypoperfused tissue quantification.International journal of computer assisted radiology and surgery · 2026Article
- Deep Learning-Based Prediction of Final Infarct Core from CT Perfusion Data: A Comparison to the Clinical Standard.Stroke (Hoboken, N.J.) · 2024Article
- Tissue Outcome Prediction in Patients with Proximal Vessel Occlusion and Mechanical Thrombectomy Using Logistic Models.Translational stroke research · 2024Article
- Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring.Medicina (Kaunas, Lithuania) · 2023Review
- Spatial CT perfusion data helpful in automatically locating vessel occlusions for acute ischemic stroke patients.Frontiers in neurology · 2023Article
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Authors and funding
10 authors.
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Abstract
objectivesTo compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps.
methodsPatients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods-a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability.
resultsA total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification.
conclusionsMultivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions. KEY POINTS: • Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions. • Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis. • A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.
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