Machine Learning–Based Preoperative Predicting TERT Promoter Mutation and EGFR Gene Amplification Phenotype in IDH Wild-Type Glioblastoma Using Advanced MR Habitat Imaging

Yan Su; Wei Guo; Yuwei Pan; Pingping He; Yang Song; Dejun She

doi:10.3174/ajnr.a9053

March 1, 2026American Journal of NeuroradiologyOpen Access

Machine Learning–Based Preoperative Predicting TERT Promoter Mutation and EGFR Gene Amplification Phenotype in IDH Wild-Type Glioblastoma Using Advanced MR Habitat Imaging

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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

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Implication

Machine learning predicts TERT promoter mutation and EGFR amplification in IDH wild-type glioblastoma, suggesting a novel imaging approach.

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.

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Cite This Study

Su et al. (2026) studied this question.

synapsesocial.com/papers/69abc1235af8044f7a4e9c48 https://doi.org/10.3174/ajnr.a9053

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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

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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