Background and purpose CT perfusion (CTP) might support decision making in patients with acute ischemic stroke by providing perfusion maps of ischemic tissue. Currently, the reliability of CTP is hampered by varying results between different post-processing software packages. The purpose of this study is to compare ischemic core volumes estimated by IntelliSpace Portal (ISP) and syngo.via with core volumes as estimated by RAPID. Methods Thirty-five CTP datasets from patients in the MR CLEAN trial were post-processed. Core volumes were estimated with ISP using default settings and with syngo.via using three different settings: default settings (method A); additional smoothing filter (method B); and adjusted settings (method C). The results were compared with RAPID. Agreement between methods was assessed using Bland–Altman analysis and intraclass correlation coefficient (ICC). Accuracy for detecting volumes up to 25 mL, 50 mL, and 70 mL was assessed. Final infarct volumes were determined on follow-up non-contrast CT. Results Median core volume was 50 mL with ISP, 41 mL with syngo.via method A, 20 mL with method B, 36 mL with method C, and 11 mL with RAPID. Agreement ranged from poor (ISP: ICC 0.41; method A: ICC 0.23) to good (method B: ICC 0.83; method C: ICC 0.85). The bias (1.8 mL) and limits of agreement (−27, 31 mL) were the smallest with syngo.via with additional smoothing (method B). Agreement for detecting core volumes ≤25 mL with ISP was 54% and 57%, 85% and 74% for syngo.via methods A, B, and C, respectively. Conclusion Best agreement with RAPID software is provided by syngo.via default settings with additional smoothing. Moreover, this method has the highest agreement in categorizing patients with small core volumes.

Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.

Background Thrombus radiomics (TR) describe complex shape and textural thrombus imaging features. We aimed to study the relationship of TR extracted from non-contrast CT with procedural and functional outcome in endovascular-treated patients with acute ischemic stroke. Methods Thrombi were segmented on thin-slice non-contrast CT (≤1 mm) from 699 patients included in the MR CLEAN Registry. In a pilot study, we selected 51 TR with consistent values across two raters’ segmentations (ICC >0.75). Random forest models using TR in addition or as a substitute to baseline clinical variables (CV) and manual thrombus measurements (MTM) were trained with 499 patients and evaluated on 200 patients for predicting successful reperfusion (extended Thrombolysis in Cerebral Ischemia (eTICI) ≥2B), first attempt reperfusion, reperfusion within three attempts, and functional independence (modified Rankin Scale (mRS) ≤2). Three texture and shape features were selected based on feature importance and related to eTICI ≥2B, number of attempts to eTICI ≥2B, and 90-day mRS with ordinal logistic regression. Results Random forest models using TR, CV or MTM had comparable predictive performance. Thrombus texture (inverse difference moment normalized) was independently associated with reperfusion (adjusted common OR (acOR) 0.85, 95% CI 0.72 to 0.99). Thrombus volume and texture were also independently associated with the number of attempts to successful reperfusion (acOR 1.36, 95% CI 1.03 to 1.88 and acOR 1.24, 95% CI 1.04 to 1.49). Conclusions TR describing thrombus volume and texture were associated with more attempts to successful reperfusion. Compared with models using CV and MTM, TR had no added value for predicting procedural and functional outcome.

Background and objective: Computed tomography perfusion (CTP) is widely used in the evaluation of acute ischemic stroke patients for endovascular thrombectomy (EVT). The stability of CTP core estimation is suboptimal and varies between software packages. We aimed to quantify the volumetric and spatial agreement between the CTP ischemic core and follow-up infarct for four ischemic core estimation approaches using syngo.via. Methods: We included successfully reperfused, EVT-treated patients with baseline CTP and 24h follow-up diffusion weighted magnetic resonance imaging (DWI) (November 2017–September 2020). Data were processed with syngo.via VB40 using four core estimation approaches based on: cerebral blood volume (CBV)<1.2mL/100mL with and without smoothing filter, relative cerebral blood flow (rCBF)<30%, and rCBF<20%. The follow-up infarct was segmented on DWI. Results: In 59 patients, median estimated CTP core volumes for four core estimation approaches ranged from 12–39 mL. Median 24h follow-up DWI infarct volume was 11 mL. The intraclass correlation coefficient (ICC) showed moderate–good volumetric agreement for all approaches (range 0.61–0.76). Median Dice was low for all approaches (range 0.16–0.21). CTP core overestimation >10mL occurred least frequent (14/59 [24%] patients) using the CBV-based core estimation approach with smoothing filter. Conclusions: In successfully reperfused patients who underwent EVT, syngo.via CTP ischemic core estimation showed moderate volumetric and spatial agreement with the follow-up infarct on DWI. In patients with complete reperfusion after EVT, the volumetric agreement was excellent. A CTP core estimation approach based on CBV<1.2 mL/100mL with smoothing filter least often overestimated the follow-up infarct volume and is therefore preferred for clinical decision making using syngo.via.

The aim of this study was to develop a convolutional neural network (CNN) that automatically detects and segments intra-arterial thrombi on baseline non-contrast computed tomography (NCCT) scans. We retrospectively collected computed tomography (CT)-scans of patients with an anterior circulation large vessel occlusion (LVO) from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands trial, both for training (n = 86) and validation (n = 43). For testing we included patients with (n = 58) and without (n = 45) an LVO from our comprehensive stroke center. Ground truth was established by consensus between two experts using both CT angiography and NCCT. We evaluated the CNN for correct identification of a thrombus, its location and thrombus segmentation and compared these with the results of a neurologist in training and expert neuroradiologist. Sensitivity of the CNN thrombus detection was 0.86, vs. 0.95 and 0.79 for the neuroradiologists. Specificity was 0.65 for the network vs. 0.58 and 0.82 for the neuroradiologists. The CNN correctly identified the location of the thrombus in 79% of the cases, compared to 81% and 77% for the neuroradiologists. The sensitivity and specificity for thrombus identification and the rate for correct thrombus location assessment by the CNN were similar to those of expert neuroradiologists.

: Introduction: Computed tomography perfusion (CTP) is variably considered to assess eligibility for endovascular thrombectomy (EVT) in acute ischemic (AIS) stroke patients. Although CTP is recommended for patient selection in later (6–24 h) time window, it is currently not recommended in the earlier (0–6 h) time window and the costs and health effects of including CTP for EVT selection remain unknown. We aim to estimate the costs and health effects of using CTP for EVT selection in AIS patients compared to conventional selection. Patients and methods: CLEOPATRA is a healthcare evaluation study using clinical and imaging data from multiple, prospective EVT trials and registries in both the earlier and later time windows. To study the long-term health and cost effects, we will construct a (“Markov”) health state transition model simulating the clinical outcome over a 5-year follow-up period for CTP-based and conventional selection for EVT. Clinical data acquired within the current study and estimates from the literature will be used as input for probabilities of events, costs, and Quality-Adjusted Life Years (QALYs) per modified Rankin Scale (mRS) subscore. Primary outcome for the cost-effectiveness analysis will be the Incremental Cost-Effectiveness Ratio (ICER) in terms of costs per QALY gained over the simulated follow-up period. Study outcomes: Outcome measures will be reported as cumulative values over a 5-year follow-up period. Discussion: This study will provide preliminary insight into costs and health effects of including CTP in the selection for EVT for AIS patients, presenting between 0 and 24 h after time last known well. The results may be used to develop recommendations and inform further implementation projects and studies.