Related Experiment Video
Updated: Feb 19, 2026
06:27
A Protocol for Explant Cultures of IDH1-mutant Diffuse Low-grade Gliomas
Published on: May 9, 2025
1.2K
Prediction of IDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade
1From the Department of Radiology (Y.W.P.), Ewha Womans University College of Medicine, Seoul, Korea.
AJNR. American Journal of Neuroradiology
|November 11, 2017
Summary
Magnetic Resonance Imaging (MRI) phenotypes can predict isocitrate dehydrogenase (IDH) mutation status in lower-grade gliomas. Imaging features help differentiate molecular subtypes, aiding in treatment decisions.
Area of Science:
- Neuro-oncology
- Radiology
- Molecular Pathology
Background:
- Lower-grade gliomas (WHO grade II) are classified into IDH-wildtype, IDH-mutant with no 1p/19q codeletion, and IDH-mutant with 1p/19q codeletion.
- These molecular subtypes exhibit distinct prognostic differences and chemosensitivity.
- Accurate molecular classification is crucial for guiding treatment strategies.
Purpose of the Study:
- To evaluate the predictive value of imaging phenotypes for molecular classification of lower-grade gliomas.
- To assess if MR imaging features can differentiate between IDH-wildtype and IDH-mutant gliomas.
- To determine the utility of the Visually AcceSAble Rembrandt Images (VASARI) lexicon in predicting glioma molecular subtypes.
Main Methods:
- MR imaging scans from 175 lower-grade glioma patients with known molecular status were analyzed.
- Imaging features were assessed using the VASARI lexicon.
- The Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify predictive imaging features for IDH1-wildtype tumors.
- The predictive model was validated in a separate cohort of 40 patients.
Main Results:
- Significant differences in imaging features were observed based on IDH1 mutation status.
- Nonlobar location, enhancing tumor proportion, multifocal distribution, and poor definition of nonenhancing margins independently predicted IDH1-wildtype status.
- The prediction model achieved AUCs of 0.859 (discovery) and 0.778 (validation).
- IDH1-mutant, 1p/19q-codeleted gliomas showed mixed/restricted diffusion and more pial invasion compared to IDH1-mutant, no codeletion gliomas.
Conclusions:
- Preoperative MR imaging phenotypes show distinct patterns correlating with molecular markers in lower-grade gliomas.
- Imaging-based prediction of IDH1-mutation status is feasible and may aid in non-invasive molecular subtyping.
- These findings suggest that radiomics can complement molecular diagnostics in neuro-oncology.
