Comparison of Normalization Techniques for Radiomics Features From Magnetic Resonance Imaging in Predicting Histologic Grade of Meningiomas

Purpose@#This study aimed to compare the effects of different normalization methods on radiomics features extracted from magnetic resonance imaging (MRI). @*Materials and Methods@#Preoperative T1-contrast enhanced MRI data from 212 patients with meningiomas were obtained from two university hospitals. The tumors were segmented using 3D Slicer software, and the PyRadiomics framework was used to extract radiomics features. We developed four experiments to predict the histological grade of meningiomas prior to surgery. The first experiment was performed without normalization.The next three experiments used the StandardScaler, MinMaxScaler, and RobustScaler to normalize radiomics features. The PyCaret framework was used for feature selection and to explore an optimized machine learning model for predicting meningioma grades. The prediction models were trained and validated using data from the first hospital. External test data from the second hospital were used to test the performance of the final models. @*Results@#Our testing results demonstrated that meningioma grade prediction performance depends highly on the choice of the normalization method. The RobustScaler demonstrated a higher level of accuracy and sensitivity than the other normalization methods. The area under the receiver operating characteristic curve and specificity of the RobustScaler method were comparable to those of no-normalization but higher than those of the Standard and MinMaxScaler methods. @*Conclusion@#The results of our study suggest that careful consideration of the normalization method may provide a way to optimize the experimental results.Keywords: Meningiomas; Radiomics features; Magnetic resonance ima

Application of Machine Learning and Deep Learning in Imaging of Ischemic Stroke

Timely analysis of imaging data is critical for diagnosis and decision-making for proper treatment strategy in the cases of ischemic stroke. Various efforts have been made to develop computer-assisted systems to improve the accuracy of stroke diagnosis and acute stroke triage. The widespread emergence of artificial intelligence technology has been integrated into the field of medicine. Artificial intelligence can play an important role in providing care to patients with stroke. In the past few decades, numerous studies have explored the use of machine learning and deep learning algorithms for application in the management of stroke. In this review, we will start with a brief introduction to machine learning and deep learning and provide clinical applications of machine learning and deep learning in various aspects of stroke management, including rapid diagnosis and improved triage, identifying large vessel occlusion, predicting time from stroke onset, automated ASPECTS (Alberta Stroke Program Early CT Score) measurement, lesion segmentation, and predicting treatment outcome. This work is focused on providing the current application of artificial intelligence techniques in the imaging of ischemic stroke, including MRI and CT.