Molecular alterations prediction in gliomas via an interpretable deep learning model: a multicentre and retrospective study.

Glioma molecular alterations predictor offered a technically feasible approach for accurate, fast, and potentially cost-effective identification of molecular alterations in resource-constrained settings.

Interpretability analysis revealed model-attended features, which improve the model's trustworthiness for clinical adoption.