An MRI Radiomics-habitat-clinical Model for Noninvasive Prediction of IDH Mutation in Gliomas With Bioinformatic Correlation: Multicenter Development With External Validation.

Rationale

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Objectives

To develop and externally validate a multicenter magnetic resonance imaging (MRI)-based model integrating radiomics, intratumoral habitat features, and clinical variables for noninvasive prediction of isocitrate dehydrogenase (IDH) mutation status in gliomas, and to explore its biological relevance.

Methods

This multicenter retrospective study included 1076 patients with histopathologically confirmed gliomas from four sets. Preoperative T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging images were analyzed. Conventional radiomic features and habitat features derived from voxel-wise clustering of multimodal MRI were extracted. After ComBat harmonization and least absolute shrinkage and selection operator selection, logistic regression models were developed in a training set and validated in three independent external sets. A nomogram was constructed for individualized prediction. Model performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis. Prognostic value was assessed by Kaplan-Meier analysis, and radiogenomic validation was performed using transcriptomic data.

Results

The combined radiomics-habitat-clinical model achieved high performance, with an AUC of 0.952 in the training set and 0.867 to 0.922 in the external test sets. The habitat model showed comparable discrimination, highlighting the robustness of habitat-derived features. SHapley Additive exPlanations analysis identified the habitat-score as the most influential predictor. The nomogram showed good calibration and clinical utility. Transcriptomic analysis of model-defined groups showed IDH-related differences in proliferation, metabolism, extracellular matrix remodeling, and immune-related pathways, providing indirect biological support for the imaging-derived signatures.

Conclusion

Integrating habitat-based analysis with radiomics enables accurate and biologically interpretable prediction of IDH mutation status in gliomas, supporting its potential for preoperative molecular stratification.