K Nael1, E Tadayon2, D Wheelwright3, A Metry2, J T Fifi3,4, S Tuhrim3, R A De Leacy4, A H Doshi2, H L Chang5, J Mocco4. 1. From the Department of Radiology (K.N., E.T., A.M., A.H.D.), Neuroimaging Advanced and Exploratory Lab Kambiznael@gmail.com. 2. From the Department of Radiology (K.N., E.T., A.M., A.H.D.), Neuroimaging Advanced and Exploratory Lab. 3. Departments of Neurology (D.W., J.F., S.T.). 4. Neurosurgery (J.F., R.A.D.L., J.M.). 5. Population Health Science and Policy (H.C.), Icahn School of Medicine at Mount Sinai, New York, New York.
Abstract
BACKGROUND AND PURPOSE: The Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis. Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke. MATERIALS AND METHODS: Patients with anterior circulation acute ischemic stroke who had baseline CTP and achieved successful recanalization were included. In a subset of patients, multiparametric voxel-based models were constructed between Bayesian-processed CTP maps and follow-up MRIs to identify pretreatment CTP parameters that were predictive of infarction using robust logistic regression. Subsequently CTP-estimated ischemic core volumes from our Bayesian model were compared against routine clinical practice oscillation singular value decomposition-relative cerebral blood flow <30%, and the volumetric accuracy was assessed against final infarct volume. RESULTS: In the constructed multivariate voxel-based model, 4 variables were identified as independent predictors of infarction: TTP, relative CBF, differential arterial tissue delay, and differential mean transit time. At an optimal cutoff point of 0.109, this model identified infarcted voxels with nearly 80% accuracy. The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from -25 to 27 mL for the Bayesian model, compared with -61 to 52 mL for oscillation singular value decomposition-relative CBF. CONCLUSIONS: We established thresholds for the Bayesian model to estimate the ischemic core. The described multiparametric Bayesian-based model improved consistency in CTP estimation of the ischemic core compared with the methodology used in current clinical routine.
BACKGROUND AND PURPOSE: The Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis. Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke. MATERIALS AND METHODS:Patients with anterior circulation acute ischemic stroke who had baseline CTP and achieved successful recanalization were included. In a subset of patients, multiparametric voxel-based models were constructed between Bayesian-processed CTP maps and follow-up MRIs to identify pretreatment CTP parameters that were predictive of infarction using robust logistic regression. Subsequently CTP-estimated ischemic core volumes from our Bayesian model were compared against routine clinical practice oscillation singular value decomposition-relative cerebral blood flow <30%, and the volumetric accuracy was assessed against final infarct volume. RESULTS: In the constructed multivariate voxel-based model, 4 variables were identified as independent predictors of infarction: TTP, relative CBF, differential arterial tissue delay, and differential mean transit time. At an optimal cutoff point of 0.109, this model identified infarcted voxels with nearly 80% accuracy. The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from -25 to 27 mL for the Bayesian model, compared with -61 to 52 mL for oscillation singular value decomposition-relative CBF. CONCLUSIONS: We established thresholds for the Bayesian model to estimate the ischemic core. The described multiparametric Bayesian-based model improved consistency in CTP estimation of the ischemic core compared with the methodology used in current clinical routine.
Authors: Jeffrey L Saver; Mayank Goyal; Alain Bonafe; Hans-Christoph Diener; Elad I Levy; Vitor M Pereira; Gregory W Albers; Christophe Cognard; David J Cohen; Werner Hacke; Olav Jansen; Tudor G Jovin; Heinrich P Mattle; Raul G Nogueira; Adnan H Siddiqui; Dileep R Yavagal; Blaise W Baxter; Thomas G Devlin; Demetrius K Lopes; Vivek K Reddy; Richard du Mesnil de Rochemont; Oliver C Singer; Reza Jahan Journal: N Engl J Med Date: 2015-04-17 Impact factor: 91.245
Authors: Branko N Huisa; William P Neil; Ronald Schrader; Marcel Maya; Benedict Pereira; Nhu T Bruce; Patrick D Lyden Journal: J Stroke Cerebrovasc Dis Date: 2012-12-14 Impact factor: 2.136
Authors: Max Wintermark; Adam E Flanders; Birgitta Velthuis; Reto Meuli; Maarten van Leeuwen; Dorit Goldsher; Carissa Pineda; Joaquin Serena; Irene van der Schaaf; Annet Waaijer; James Anderson; Gary Nesbit; Igal Gabriely; Victoria Medina; Ana Quiles; Scott Pohlman; Marcel Quist; Pierre Schnyder; Julien Bogousslavsky; William P Dillon; Salvador Pedraza Journal: Stroke Date: 2006-03-02 Impact factor: 7.914
Authors: William A Copen; Livia T Morais; Ona Wu; Lee H Schwamm; Pamela W Schaefer; R Gilberto González; Albert J Yoo Journal: PLoS One Date: 2015-07-20 Impact factor: 3.240
Authors: Ralph R E G Geuskens; Jordi Borst; Marit Lucas; A M Merel Boers; Olvert A Berkhemer; Yvo B W E M Roos; Marianne A A van Walderveen; Sjoerd F M Jenniskens; Wim H van Zwam; Diederik W J Dippel; Charles B L M Majoie; Henk A Marquering Journal: PLoS One Date: 2015-11-04 Impact factor: 3.240
Authors: 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 Journal: Eur Radiol Date: 2022-03-31 Impact factor: 7.034