TY - GEN
T1 - A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features
AU - Zhang, Haoyue
AU - Polson, Jennifer
AU - Nael, Kambiz
AU - Salamon, Noriko
AU - Yoo, Bryan
AU - Speier, William
AU - Arnold, Corey
N1 - Funding Information:
*Haoyue Zhang and Jennifer S Polson contributed equally. This work was supported by the following grants: NIH T32EB016640-07, NIH R01NS100806-02, and NVIDIA Academic Hardware Grant. H. Zhang, J. Polson, and W. Speier are with the Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA. K. Nael, N. Salamon, and B. Yoo are with the Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA. C. Arnold is with the Departments of Bioengineering, Radiology, and Pathology, University of California, Los Angeles, Los Angeles, CA, USA (Email: cwarnold@ucla.edu, Phone: (310) 794-3538).
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instruct the use of pretreatment imaging to characterize a patient's cerebrovascular flow, as there are many factors that may underlie a patient's successful response to treatment. There is a critical need to leverage pretreatment imaging, taken at admission, to guide potential treatment avenues in an automated fashion. The aim of this study is to develop and validate a fully automated machine learning algorithm to predict the final modified thrombolysis in cerebral infarction (mTICI) score following MTB. A total 321 radiomics features were computed from segmented pretreatment MRI scans for 141 patients. Successful recanalization was defined as mTICI score >= 2c. Different feature selection methods and classification models were examined in this study. Our best performance model achieved 74.42±2.52% AUC, 75.56±4.44% sensitivity, and 76.75±4.55% specificity, showing a good prediction of reperfusion quality using pretreatment MRI. Results suggest that MR images can be informative to predicting patient response to MTB, and further validation with a larger cohort can determine the clinical utility.
AB - Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instruct the use of pretreatment imaging to characterize a patient's cerebrovascular flow, as there are many factors that may underlie a patient's successful response to treatment. There is a critical need to leverage pretreatment imaging, taken at admission, to guide potential treatment avenues in an automated fashion. The aim of this study is to develop and validate a fully automated machine learning algorithm to predict the final modified thrombolysis in cerebral infarction (mTICI) score following MTB. A total 321 radiomics features were computed from segmented pretreatment MRI scans for 141 patients. Successful recanalization was defined as mTICI score >= 2c. Different feature selection methods and classification models were examined in this study. Our best performance model achieved 74.42±2.52% AUC, 75.56±4.44% sensitivity, and 76.75±4.55% specificity, showing a good prediction of reperfusion quality using pretreatment MRI. Results suggest that MR images can be informative to predicting patient response to MTB, and further validation with a larger cohort can determine the clinical utility.
KW - Machine learning
KW - Radiomics
KW - Stroke treatment
KW - Structural MRI
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U2 - 10.1109/BHI50953.2021.9508597
DO - 10.1109/BHI50953.2021.9508597
M3 - Conference contribution
AN - SCOPUS:85125504381
T3 - BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings
BT - BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021
Y2 - 27 July 2021 through 30 July 2021
ER -