Title | ||
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A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features |
Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.1109/BHI50953.2021.9508597 | 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) |
Keywords | DocType | ISSN |
Structural MRI,Radiomics,Machine Learning,Stroke Treatment | Conference | 2641-3590 |
ISBN | Citations | PageRank |
978-1-6654-4770-6 | 0 | 0.34 |
References | Authors | |
0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Haoyue Zhang | 1 | 2 | 1.40 |
Jennifer Polson | 2 | 1 | 1.36 |
Kambiz Nael | 3 | 0 | 0.34 |
Noriko Salamon | 4 | 0 | 0.34 |
Bryan Yoo | 5 | 0 | 0.34 |
William Speier | 6 | 38 | 7.39 |
Corey W. Arnold | 7 | 0 | 0.34 |