Deepthi Rajashekar1,2,3, Pauline Mouchès1,2,3, Jens Fiehler4, Bijoy K Menon2,3,5,6, Mayank Goyal2,3, Andrew M Demchuk2,3,5, Michael D Hill2,3,5,6,7, Sean P Dukelow5,6, Nils D Forkert2,3,5. 1. Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada. 2. Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. 3. 157742Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada. 4. Department of Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 5. Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. 6. Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada. 7. Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Abstract
BACKGROUND AND PURPOSE: Clinical assessment scores in acute ischemic stroke are only moderately correlated with lesion volume since lesion location is an important confounding factor. Many studies have investigated gray matter indicators of stroke severity, but the understanding of white matter tract involvement is limited in the early phase after stroke. This study aimed to measure and model the involvement of white matter tracts with respect to 24-h post-stroke National Institutes of Health Stroke Scale (NIHSS). MATERIAL AND METHODS: A total of 96 patients (50 females, mean age 66.4 ± 14.0 years, median NIHSS 5, interquartile range: 2-9.5) with follow-up fluid-attenuated inversion recovery magnetic resonance imaging data sets acquired one to seven days after acute ischemic stroke onset due to proximal anterior circulation occlusion were included. Lesions were semi-automatically segmented and non-linearly registered to a common reference atlas. The lesion overlap and tract integrity were determined for each white matter tract in the AALCAT atlas and used to model NIHSS outcomes using a supervised linear-kernel support vector regression method, which was evaluated using leave-one-patient-out cross validation. RESULTS: The support vector regression model using the tract integrity and tract lesion overlap measurements predicted the 24-h NIHSS score with a high correlation value of r = 0.7. Using the tract overlap and tract integrity feature improved the modeling accuracy of NIHSS significantly by 6% (p < 0.05) compared to using overlap measures only. CONCLUSION: White matter tract integrity and lesion load are important predictors for clinical outcome after an acute ischemic stroke as measured by the NIHSS and should be integrated for predictive modeling.
BACKGROUND AND PURPOSE: Clinical assessment scores in acute ischemic stroke are only moderately correlated with lesion volume since lesion location is an important confounding factor. Many studies have investigated gray matter indicators of stroke severity, but the understanding of white matter tract involvement is limited in the early phase after stroke. This study aimed to measure and model the involvement of white matter tracts with respect to 24-h post-stroke National Institutes of Health Stroke Scale (NIHSS). MATERIAL AND METHODS: A total of 96 patients (50 females, mean age 66.4 ± 14.0 years, median NIHSS 5, interquartile range: 2-9.5) with follow-up fluid-attenuated inversion recovery magnetic resonance imaging data sets acquired one to seven days after acute ischemic stroke onset due to proximal anterior circulation occlusion were included. Lesions were semi-automatically segmented and non-linearly registered to a common reference atlas. The lesion overlap and tract integrity were determined for each white matter tract in the AALCAT atlas and used to model NIHSS outcomes using a supervised linear-kernel support vector regression method, which was evaluated using leave-one-patient-out cross validation. RESULTS: The support vector regression model using the tract integrity and tract lesion overlap measurements predicted the 24-h NIHSS score with a high correlation value of r = 0.7. Using the tract overlap and tract integrity feature improved the modeling accuracy of NIHSS significantly by 6% (p < 0.05) compared to using overlap measures only. CONCLUSION: White matter tract integrity and lesion load are important predictors for clinical outcome after an acute ischemic stroke as measured by the NIHSS and should be integrated for predictive modeling.
Entities:
Keywords:
Acute ischemic stroke; computer-assisted image analysis; distance mapping transform; support vector machines; white matter
Authors: Deepthi Rajashekar; Matthias Wilms; M Ethan MacDonald; Serena Schimert; Michael D Hill; Andrew Demchuk; Mayank Goyal; Sean P Dukelow; Nils Daniel Forkert Journal: Stroke Vasc Neurol Date: 2021-11-25
Authors: Johannes Kaesmacher; Mirjam Kaesmacher; Maria Berndt; Christian Maegerlein; Sebastian Mönch; Silke Wunderlich; Thomas R Meinel; Urs Fischer; Claus Zimmer; Tobias Boeckh-Behrens; Justus F Kleine Journal: Stroke Date: 2021-04-08 Impact factor: 7.914