Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.

ABSTRACT:

Purpose

Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.

Methods

Eight classical machine learning methods were evaluated in terms of their stability and performance for pre-operative IDH genotype prediction. A total of 126 patients were enrolled for analysis. Overall, 704 radiomic features extracted from the pre-operative MRI images were analyzed. The patients were randomly assigned to either the training set or the validation set at a ratio of 2:1. Feature selection and classification model training were done using the training set, whereas the predictive performance and stability of th

SUBMITTER: Wu S 

PROVIDER: S-EPMC6394679 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature