Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers.

ABSTRACT: This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selectin

SUBMITTER: Song D 

PROVIDER: S-EPMC8460834 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature