Ms. Samantha E. Seymour Profile

Samantha E. Seymour

at Canon Stroke and Vascular Research Ctr.

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Publications (3)

Proceedings Article | 7 April 2023 Poster + Paper

Prediction of hematoma expansion using a random forest model with clinical data of patients with intracerebral hemorrhage

Akihiro Taguchi, Samantha Seymour, Ciprian Ionita, Kurt Schultz, Ryo Shiroishi

Proceedings Volume 12465, 124651Z (2023) https://doi.org/10.1117/12.2653401

KEYWORDS: Data modeling, Random forests, Brain, Machine learning, Education and training, Computed tomography, Algorithm development, Statistical analysis, Performance modeling

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Predicting hematoma expansion after spontaneous intracranial hemorrhage through a radiomics based model

Samantha Seymour, Ryan Rava, Dennis Swetz, Andre Montiero, Ammad Baig, Kurt Schultz, Kenneth Snyder, Muhammad Waqas, Jason Davies, Elad Levy, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12033, 120332X (2022) https://doi.org/10.1117/12.2611847

KEYWORDS: Magnetic resonance imaging, Lawrencium, Feature extraction, Medical imaging, Image segmentation, Computed tomography, Systems modeling, Process modeling, Monte Carlo methods, Data acquisition

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Purpose: Intracranial hemorrhage (ICH) is characterized as bleeding into the brain tissue, intracranial space, and ventricles and is the second most disabling form of stroke. Hematoma expansion (HE) following ICH has been correlated with significant neurological decline and death. For early detection of patients at risk, deep learning prediction models were developed to predict whether hematoma due to ICH will expand. This study aimed to explore the feasibility of HE prediction using a radiomic approach to help clinicians better stratify HE patients and tailor intensive therapies timely and effectively. Materials and Methods: Two hundred ICH patients with known hematoma evolution, were enrolled in this study. An open-source python package was utilized for the extraction of radiomic features from both non-contrast computed tomography (NCCT) and magnetic resonance imaging (MRI) scans through characterization algorithms. A total of 99 radiomic features were extracted and different features were selected for network inputs for the NCCT and MR models. Seven supervised classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron were used to build the models. A training:testing split of 80:20 and 20 iterations of Monte Carlo cross validation were performed to prevent overfitting and assess the variability of the networks, respectively. The models were fed training datasets from which they learned to classify the data based on pre-determined radiomic categories. Results: The highest sensitivity among the NCCT classifier models was seen with the support vector machine (SVM) and logistic regression (LR) of 72 ± 0.3% and 73 ± 0.5%, respectively. The MRI classifier models had the highest sensitivity of 68 ± 0.5% and 72 ± 0.5% for the SVM and LR models, respectively. Conclusions: This study indicates that the NCCT radiomics model is a better predictor of HE and that SVM and LR classifiers are better predictors of HE due to their more cautious approach indicated by a higher sensitivity metric.

Proceedings Article | 4 April 2022 Presentation + Paper

Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning

Dennis Swetz, Samantha Seymour, Ryan Rava, Mohammad Mahdi Shiraz Bhurwani, Andre Monteiro, Ammad Baig, Muhammad Waqas, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12036, 120360B (2022) https://doi.org/10.1117/12.2610672

KEYWORDS: Machine learning, Brain, Computed tomography, Blood, Data modeling, Receivers, Performance modeling, Statistical analysis, Algorithm development

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