CT Perfusion and Multiphase CT Angiography in Malignant Brain Edema Prediction in Patients with Acute Ischemic Stroke

2016 ◽  
Vol 79/112 (2) ◽  
pp. 213-217
Author(s):  
Ondřej Volný ◽  
Petra Cimflová ◽  
C.D. D´esterre
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Brain Edema ◽  
Ct Angiography ◽  
Ct Perfusion ◽  
Multiphase Ct

scholarly journals Assessing the Relative Value of CT Perfusion Compared to Non-contrast CT and CT Angiography in Prognosticating Reperfusion-Eligible Acute Ischemic Stroke Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Bivard ◽  
Christopher Levi ◽  
Longting Lin ◽  
Xin Cheng ◽  
Richard Aviv ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Predictive Accuracy ◽  
Ct Perfusion ◽  
Imaging Modality ◽  
Characteristic Curve ◽  
Treatment Decision ◽  
Receiver Operator Characteristic Curve ◽  
Stroke Patients

In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were included in this study. A model including clinical variables, alteplase treatment, and NCCT ASPECTS was weak (R2 0.067, P < 0.001, AUC 0.605) at predicting 90 day mRS. A second model, including dynamic CTA variables (collateral grade, occlusion severity) showed better predictive accuracy for patient outcome (R2 0.381, P < 0.001, AUC 0.781). A third model incorporating CTP variables showed very high predictive accuracy (R2 0.488, P < 0.001, AUC 0.899). Combining all three imaging modalities variables also showed good predictive accuracy for outcome but did not improve on the CTP model (R2 0.439, P < 0.001, AUC 0.825). CT perfusion predicts patient outcomes from alteplase therapy more accurately than models incorporating NCCT and/or CT angiography. This data has implications for artificial intelligence or machine learning models.

scholarly journals Multiphase CT Angiography: A New Tool for the Imaging Triage of Patients with Acute Ischemic Stroke

Radiology ◽  
2015 ◽  
Vol 275 (2) ◽  
pp. 510-520 ◽  
Author(s):  
Bijoy K. Menon ◽  
Christopher D. d’Esterre ◽  
Emmad M. Qazi ◽  
Mohammed Almekhlafi ◽  
Leszek Hahn ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Multiphase Ct

The Prognostic Value of CT Angiography and CT Perfusion in Acute Ischemic Stroke

2015 ◽  
Vol 40 (5-6) ◽  
pp. 258-269 ◽  
Author(s):  
Tom van Seeters ◽  
Geert Jan Biessels ◽  
L. Jaap Kappelle ◽  
Irene C. van der Schaaf ◽  
Jan Willem Dankbaar ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Predictive Value ◽  
Prognostic Value ◽  
Ct Perfusion ◽  
Patient Characteristics ◽  
Poor Clinical Outcome ◽  
Poor Outcome

Background: CT angiography (CTA) and CT perfusion (CTP) are important diagnostic tools in acute ischemic stroke. We investigated the prognostic value of CTA and CTP for clinical outcome and determined whether they have additional prognostic value over patient characteristics and non-contrast CT (NCCT). Methods: We included 1,374 patients with suspected acute ischemic stroke in the prospective multicenter Dutch acute stroke study. Sixty percent of the cohort was used for deriving the predictors and the remaining 40% for validating them. We calculated the predictive values of CTA and CTP predictors for poor clinical outcome (modified Rankin Scale score 3-6). Associations between CTA and CTP predictors and poor clinical outcome were assessed with odds ratios (OR). Multivariable logistic regression models were developed based on patient characteristics and NCCT predictors, and subsequently CTA and CTP predictors were added. The increase in area under the curve (AUC) value was determined to assess the additional prognostic value of CTA and CTP. Model validation was performed by assessing discrimination and calibration. Results: Poor outcome occurred in 501 patients (36.5%). Each of the evaluated CTA measures strongly predicted outcome in univariable analyses: the positive predictive value (PPV) was 59% for Alberta Stroke Program Early CT Score (ASPECTS) ≤7 on CTA source images (OR 3.3; 95% CI 2.3-4.8), 63% for presence of a proximal intracranial occlusion (OR 5.1; 95% CI 3.7-7.1), 66% for poor leptomeningeal collaterals (OR 4.3; 95% CI 2.8-6.6), and 58% for a >70% carotid or vertebrobasilar stenosis/occlusion (OR 3.2; 95% CI 2.2-4.6). The same applied to the CTP measures, as the PPVs were 65% for ASPECTS ≤7 on cerebral blood volume maps (OR 5.1; 95% CI 3.7-7.2) and 53% for ASPECTS ≤7 on mean transit time maps (OR 3.9; 95% CI 2.9-5.3). The prognostic model based on patient characteristics and NCCT measures was highly predictive for poor clinical outcome (AUC 0.84; 95% CI 0.81-0.86). Adding CTA and CTP predictors to this model did not improve the predictive value (AUC 0.85; 95% CI 0.83-0.88). In the validation cohort, the AUC values were 0.78 (95% CI 0.73-0.82) and 0.79 (95% CI 0.75-0.83), respectively. Calibration of the models was satisfactory. Conclusions: In patients with suspected acute ischemic stroke, admission CTA and CTP parameters are strong predictors of poor outcome and can be used to predict long-term clinical outcome. In multivariable prediction models, however, their additional prognostic value over patient characteristics and NCCT is limited in an unselected stroke population.

scholarly journals Validation of collateral scoring on flat-detector multiphase CT angiography in patients with acute ischemic stroke

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202592 ◽  
Author(s):  
Ilko L. Maier ◽  
Fabien Scalzo ◽  
Johanna R. Leyhe ◽  
Katharina Schregel ◽  
Daniel Behme ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Flat Detector ◽  
Multiphase Ct

scholarly journals Automated Prediction of Ischemic Brain Tissue Fate from Multi-Phase CT-Angiography in Patients with Acute Ischemic Stroke Using Machine Learning

2020 ◽  
Author(s):  
Wu Qiu ◽  
Hulin Kuang ◽  
Johanna Ospel ◽  
Michael D Hill ◽  
Andrew Demchuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Correlation Coefficient ◽  
Ct Angiography ◽  
Tissue Perfusion ◽  
Dice Similarity Coefficient ◽  
Derivation Cohort ◽  
Multiphase Ct

Background: Multiphase CT-Angiography (mCTA) provides time variant images of the pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict infarct, penumbra and tissue perfusion from mCTA source images. Methods: 284 patients with AIS were included from the PRoveIT study. All patients had non-contrast CT, mCTA and CTP imaging at baseline and follow up MRI/NCCT imaging. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict infarct, penumbra, and perfusion parameter on CTP, respectively. The remaining unseen 144 patient images independent of the derivation cohort were used to test the derived ML models. The predicted infarct, penumbra, and perfusion volume from ML models was spatially and volumetrically compared to manually contoured follow up infarct and time-dependent Tmax thresholded volume (CTP volume), using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient (DSC). Results: Within the test cohort, Bland-Altman plots showed that the mean difference between the mCTA predicted infarct and follow up infarct was 21.7 mL (limit of agreement (LoA): -41.0 to 84.3mL) in the 100 patients who had acute reperfusion (mTICI 2b/2c/3), and 3.4mL (LoA: -66 to 72.9mL) in the 44 patients who did not achieve reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 [95%CI: 0.15-0.55, P<.01] and ICC 0.42 [95% CI: 0.18-0.50, P<.01]; in non-reperfused subjects CCC was 0.52 [95%CI: 0.2-0.6, P<.001] and ICC 0.6 [95% CI: 0.37-0.76, P<.001]. No difference was observed between the mCTA and CTP predicted infarct volume for the overall test cohort (P=.67). Conclusion: Multiphase CT Angiography is able to predict infarct, penumbra and tissue perfusion, comparable to CT perfusion imaging.

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