Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

ABSTRACT:

Background

Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas.

Methods

This single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The Tree-based Pipeline Optimization Tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomi

SUBMITTER: Su X 

PROVIDER: S-EPMC7442326 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature