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Abstract #4892

 Predicting Hematoma Expansion after Spontaneous Intracranial Hemorrhage Through a Magnetic Resonance-Based Radiomics Model

Samantha E Seymour1, Ryan A Rava1, Mitchell T Chudzik1, Kenneth V Snyder1,2,3, Muhammad E Waqas1,3, Jason M Davies1,2,3,4, Elad E Levy1,2,3, Adnan E Siddiqui1,2,3, Xiaoliang E Zhang5, and Ciprian E Ionita1,2,3,6
1Canon Stroke and Vascular Center, Buffalo, NY, United States, 2Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States, 3Department of Neurosurgery, University at Buffalo, Buffalo, NY, United States, 4Department of Bioinformatics, University at Buffalo, Buffalo, NY, United States, 5Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States, 6Department of Biomedical Engineerinng, University at Buffalo, Buffalo, NY, United States


Intracranial hemorrhage (ICH) is bleeding within the cranium and occurs within the brain tissue, ventricles, and intracranial space. Hematoma expansion following an ICH has been related to increased mortality and morbidity inpatients. To detect ICH patients at risk, machine learning models can be used to predict whether or not hematoma expansion will occur. This study aims to assess the feasibility of machine learning prediction models using a radiomics approach. The highest sensitivity results indicated as 95% confidence intervals are 0.68 ± 0.004 and 0.72 ± 0.004, were achieved by support vector machine and logistic regression classifier models, respectively.

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