Machine Learning–Based Preoperative Predicting TERT Promoter Mutation and EGFR Gene Amplification Phenotype in IDH Wild-Type Glioblastoma Using Advanced MR Habitat Imaging | Synapse
March 7, 2026Open Access
Machine Learning–Based Preoperative Predicting TERT Promoter Mutation and EGFR Gene Amplification Phenotype in IDH Wild-Type Glioblastoma Using Advanced MR Habitat Imaging
Key Points
This research aims to develop a machine learning method for predicting TERT mutation and EGFR amplification in glioblastoma using advanced MRI.
Utilized advanced MRI for tumor habitat imaging
Developed machine learning models for predictions
Analyzed TERT promoter mutation and EGFR amplification status in IDH wild-type glioblastoma
The imaging model accurately predicted TERT promoter mutations
The model effectively determined EGFR amplification status
Findings support the use of MRI in glioblastoma genetic profiling
Abstract
The tumor habitat imaging model based on advanced MRI was useful for accurately predicting TERT promoter mutation and EGFR amplification status in IDH wild-type glioblastoma.