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Visual versus automated CT perfusion for outcome prediction in anterior circulation stroke treated with mechanical thrombectomy Cover

Visual versus automated CT perfusion for outcome prediction in anterior circulation stroke treated with mechanical thrombectomy

Open Access
|Jul 2026

Abstract

Background

The prognostic value of simple visual assessment of CT perfusion (CTP) for predicting clinical outcomes has already been demonstrated in stroke patients undergoing mechanical thrombectomy (MT). Automated software, however, has become the dominant method of CTP evaluation. The aim of the study was to compare the prognostic accuracy of visually graded CTP maps with automated CTP analysis on clinical outcomes in anterior circulation stroke patients treated with MT.

Patients and methods

In the single centre we retrospectively analysed consecutive patients with anterior circulation large-vessel occlusion treated with MT. Baseline imaging included non-contrast brain CT, CT angiography (CTA) with collateral status (CS) and CTP. Time to peak (TTP) maps were visually graded on a four-level ordinal scale based on estimated penumbra. Automated CTP was processed with syngo.via (SYV) using default thresholds and classified on a comparable ordinal scale. The primary endpoint was favourable 90-day functional outcome (Rankin Scale (mRS) ≤ 3).

Results

We included 579 patients. Agreement between visual and automated CTP was fair (κ = 0.36). Both methods were significantly associated with favourable outcome (p < 0.01), with visual grading showing a slightly stronger association (Pearson χ2 = 30 vs. 22). In multivariable models, visual CTP remained an independent predictor, whereas automated CTP lost significance when CS was included.

Conclusions

Visual CTP grading provided a slightly better prognostic performance than automated CTP. Visual CTP assessment is a simple, accessible and clinically meaningful tool for both treatment decision making and outcome prediction in anterior circulation stroke treated with MT.

DOI: https://doi.org/10.2478/raon-2026-0039 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
Language: English
Submitted on: Mar 3, 2026
Accepted on: Apr 18, 2026
Published on: Jul 29, 2026
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2026 Igor Rigler, Tina Gspan, Alja Longo, Janja Pretnar Oblak, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.