Explainable Generative Modeling for IDH Mutation Prediction in Glioma: A Conditional VAE with Counterfactual Synthesis and Interpretability

Glioma is the most common primary malignant brain tumor in adults, and accurate determination of isocitrate dehydrogenase (IDH) mutation status is important for diagnosis, treatment planning, and prognostic assessment. Currently, definitive IDH status determination relies on molecular analysis of tumor tissue obtained through biopsy or surgical resection, motivating the development of accurate, interpretable, and non-invasive imaging biomarkers. This study proposes an explainable multimodal generative framework for IDH mutation prediction using multiparametric magnetic resonance imaging (MRI), radiomics, and clinical information. The framework employs a self-supervised 3D convolutional neural network (CNN) to learn imaging representations, a dedicated encoder for radiomic features, and late fusion to construct a unified multimodal representation. A Conditional Variational Autoencoder (CVAE) is used for minority-class feature-space augmentation, while a Generalized Additive Model with Interactions (GAMI-Net) provides intrinsically interpretable classification without relying on post hoc explanation methods. Experiments were conducted on 622 patients with diffuse glioma from the University of Texas Southwestern Medical Center, comprising a training cohort of 380 patients, a validation cohort of 96 patients, and a held-out temporal test cohort of 146 patients. On the temporal test cohort, the proposed framework achieved an area under the receiver operating characteristic curve (AUC) of 0.9519 (95% CI: 0.9158–0.9802), an accuracy of 87.7%, and a macro F1-score of 0.847, with higher discrimination than the evaluated DenseNet169-3D and ConvNeXt imaging-only baselines. Intrinsic explainability analysis identified contributions from imaging, radiomic, and clinical information, while global effect functions revealed nonlinear relationships between model features and predicted IDH mutation probability. External evaluation on an independent UCSD cohort showed lower discrimination (AUC = 0.675, 95% CI: 0.513–0.822), highlighting a substantial cross-institutional generalization gap. Overall, the findings demonstrate that the proposed framework combines strong internal temporal discrimination with intrinsic and generative interpretability, while the external results emphasize the need for further multi-center validation and improved robustness to domain shift before clinical deployment.