Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.

ABSTRACT: INTRODUCTION:In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction. MATERIALS AND METHODS:Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion

SUBMITTER: Grosser M 

PROVIDER: S-EPMC6980585 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature