Variability of Ischemic Core and Penumbra using CT Perfusion in Acute Ischemic Stroke M. Reddy, A. Livorine, R. Naini, H. Sucharew, A. Vagal University of Cincinnati Neuroscience Institute Poster No: EP-65 Control No: 1041

Disclosures Mahati Reddy : None Achala Vagal : CCTST CT2 Research Award PI, Imaging Core Lab, PRISMS trial, Genentech, Inc. Anthony Livorine : None Rohit Naini : None Heidi Sucharew : None

Purpose Objectives : To assess the intra-observer variability for quantifying ischemic core and penumbra values using automated, semi-automated or manual post processing techniques To assess the variability for different deconvolution algorithms using a commercially available perfusion package

Materials 30 randomly selected ischemic stroke patients from Interventional Management of Stroke (IMS III) trial data set Post processing techniques using Olea Medical® version 2.3: Manual Automated Semi – Automated Deconvolution algorithms: Standard Singular Value Decompostion (sSVD) Block Circulant Matrix Singular Value Decomposition (cSVD) Oscillation Index Based Singular Value Decomposition (oSVD) Bayesian Estimation

Methods CTP analysis was performed by a single observer using a commercially available software Penumbral Volumes were calculated using a threshold of Tmax > 6 Ischemic Core value was calculated using dual threshold with relative CBF < 30 % and Tmax > 6 MTT CBF

Methods Single Value Decomposition (SVD) is an algebraic process by which the MTT maps are deconvolved from time-concentration curves for an arterial / venous region of interest. sSVD : measurement of CBF using intravascular tracer bolus passage; sensitive tracer delay effect cSVD : estimates perfusion parameters independent of delay of contrast in the AIF and arrival of contrast in the tissue oSVD : iterative method which repeats the cSVD process until oscillation in the residue function is below a threshold Bayesian estimation directly estimates residue function of brain tissues by applying Bayesian probability theory on the intravascular tracer model. It is inherently tracer-delay insensitive. Sasaki, Makoto, et al. "Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom." Neuroradiology 55.10 (2013): 1197-1203. Bjørnerud, Atle, and Kyrre E. Emblem. "A fully automated method for quantitative cerebral hemodynamic analysis using DSC–MRI." Journal of Cerebral Blood Flow & Metabolism 30.5 (2010): 1066-1078.

Methods Volumes calculated using three different post processing techniques Manual = manual selection of arterial input function (AIF) and venous input function (VOF) Semi-automated = allows user adjustment of AIF and VOF when deemed appropriate Automated = automatic selection of AIF and VOF AIF VOF

Methods + 5 patients excluded due to : motion artifact Truncated Time Curve 5 patients excluded due to : motion artifact significantly truncated time curves + = Motion Artifact

Methods Bland – Altman Analysis Quantified variability with Bland-Altman analysis1 using repeatability coefficient and coefficient of variation Bland – Altman Analysis Identify relative difference between 2 observations by: Plotting difference between the numerical value vs the numerical mean of the 2 values 2 1. Bland, J. Martin, and DouglasG Altman. "Statistical methods for assessing agreement between two methods of clinical measurement." The lancet327.8476 (1986): 307-310. 2. Waaijer, A., et al. "Reproducibility of quantitative CT brain perfusion measurements in patients with symptomatic unilateral carotid artery stenosis."American journal of neuroradiology 28.5 (2007): 927-932.

Lower Repeatability Coefficient Lower Coefficient of Variation Methods Repeatability Coefficient = When comparing 2 methods, repeatability coefficient is a threshold within which 95 % of the absolute difference values lie. Lower Repeatability Coefficient Better Agreement Coefficient of Variation = When comparing 2 methods, coefficient of variation is the ratio of repeatability coefficient over mean of the two values. Lower Coefficient of Variation Better Agreement 1. Bland, J. Martin, and DouglasG Altman. "Statistical methods for assessing agreement between two methods of clinical measurement." The lancet327.8476 (1986): 307-310. 2. Soares, Bruno P., et al. "Automated versus manual post-processing of perfusion-CT data in patients with acute cerebral ischemia: influence on interobserver variability." Neuroradiology 51.7 (2009): 445-451.

Automated vs Semi-Automated Manual vs Semi-Automated Results VERY HIGH VARIABILITY was observed in ischemic core quantification in all three post processing techniques (manual, semi-automated and automated)   Sample Mean Standard Deviation Repeatability Variability Variability at 80 % CI Automated vs Manual 7.02 8.40 8.76 124.74% 27.39% Automated vs Semi-Automated 7.46 9.13 12.14 162.79% 37.37% Manual vs Semi-Automated 7.01 8.33 10.02 143.02% 34.38%

Automated vs Semi-Automated Manual vs Semi-Automated Results Variability in PENUMBRAL volumes is LOWER than variability in CORE volumes with greater agreement in Automated technique.   Sample Mean Standard Deviation Repeatability Variability Variability at 80 % CI Automated vs Manual 51.88 35.16 18.49 35.63% 17.76% Automated vs Semi-Automated 52.28 34.40 5.11 9.77% 4.77% Manual vs Semi-Automated 52.11 35.59 18.57 17.06%

Results VERY HIGH VARIABILITY was observed in ischemic core quantification comparing various deconvolution methods   Sample Mean Standard Deviation Repeatability Variability Variability at 80% CI oSVD vs sSVD 10.14 10.18 11.92 117.57% 83.25% oSVD vs cSVD 6.22 7.98 10.47 168.26% 40.20% oSVD vs Bayesian 15.63 13.37 24.78 158.58% 117.46% sSVD vs cSVD 8.92 9.11 13.13 147.21% 91.28% sSVD vs Bayesian 18.33 14.50 18.35 100.11% 61.04% cSVD vs Bayesian 14.41 12.29 28.37 196.89% 125.46%

Results Variability in PENUMBRAL volumes is LOWER than variability in CORE volumes comparing different deconvolution methods.   Sample Mean Standard Deviation Repeatability Variability Variability at 80% CI oSVD vs sSVD 50.33 33.53 11.72 23.29% 15.25% oSVD vs cSVD 53.18 35.77 12.42 23.36% 12.28% oSVD vs Bayesian 12.43 sSVD vs cSVD 51.00 34.46 20.11 39.42% 15.45% sSVD vs Bayesian 15.47% cSVD vs Bayesian 53.86 36.71 0.01 0.01% 0%

Results Bland – Altman plots were generated for comparison of each variable. It is to be noted that 20 % or less of the data contributes to a majority of the variability. For example, Bland-Altman plot for measurement of ischemic core using manual vs automated post-processing technique is shown below: These two outliers significantly increase the calculated variability between two post processing techniques. Therefore, variability at 80% Confidence Interval (CI) was calculated to reduce the effect of these outliers.

Limitations Our data is limited by a small sample size. Intra-observer variability was measured using a single post-processing software. Caution should be exercised before results are directly extended to other vendors / softwares. Calculated variability in core values may appear worse than penumbral values due to relative small volumes, therefore exaggerating the effects of small errors. Ideal threshold of Tmax used to differentiate penumbral versus benign oligemic volume is lacking. Changing the Tmax threshold could affect variability.

Conclusions There is high variability in CTP parameters, particularly ischemic core measurements among various post processing techniques (manual, semi-automated and automated). There is high variability in CTP parameters among various deconvolution algorithms used in perfusion post processing. The study highlights the challenges for using CTP as a decision-making tool in acute stroke and emphasizes a critical need for standardization of CTP analysis before it can be integrated in routine clinical practice.