Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

Daan Peerlings*, Fasco van Ommen, Edwin Bennink, Jan W. Dankbaar, Birgitta K. Velthuis, Bart J. Emmer, Jan W. Hoving, Charles B. L. M. Majoie, Henk A. Marquering, Hugo W. A. M. de Jong

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Objectives: To compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps. Methods: Patients 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. Results: A 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. Conclusions: Multivariable 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.
Original languageEnglish
Pages (from-to)6367-6375
Number of pages9
JournalEuropean Radiology
Issue number9
Early online date2022
Publication statusPublished - Sept 2022

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