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This is a Quantitative Study study.

September 17, 2025

Three-Class Radiomics Models for IDH-Mutation and 1p19q-Codeletion Status Prediction in Adult-type Diffuse Gliomas

Key Points

The model predicts idh mutation and 1p19q-codeletion status with high accuracy, achieving an overall accuracy of 0.907.
Support vector machine classifiers significantly outperform other models, reaching AUCs of 0.963, 0.964, and 0.925 for different genetic subtypes.
MRI radiomics offers a non-invasive method for preoperative genetic prediction, which may improve glioma patient management.
The predictive models were validated with internal and external datasets, emphasizing their robustness across different cohorts.

Abstract

Motivation: Glioma patients with different IDH mutation and 1p19q-codeletion status indicate different treatment responses and prognosis. Goal(s): To predict the genetic subtypes of adult-type diffuse gliomas with three-class MRI radiomics. Approach: MRI based three-class radiomics models were used to predict the IDH mutation and 1p19q-codeletion status. Results: Support vector machine (SVM) classifiers combined with conventional MR sequences based on tumor ROI was proved to be the best performing diagnostic model. The model achieved area under the curve (AUC) of 0.963, 0.964, 0.925 for IDHwt, IDHmut-intact and IDHmut-codel prediction, with overall accuracy of 0.907. The model was validated both in internal and external validation set. Impact: Three-class MRI radiomics can preoperatively predict IDH and 1p19q-codeletion with satisfied performance, which is helpful for glioma risk stratification.

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Three-Class Radiomics Models for IDH-Mutation and 1p19q-Codeletion Status Prediction in Adult-type Diffuse Gliomas

Key Points

Abstract

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