IDH Genotyping and Glioma Prognosis Research Based on an Interpretable Transformer Learning Framework

Accurate genotyping and prognosis of glioma patients present significant clinical challenges, often dependent on subjective judgement and insufficient scientific evidence. This study aims to develop a robust, noninvasive preoperative multi‐modal MRI‐based transformer learning model to predict IDH genotyping and glioma prognosis. This multi‐centre study included 563 glioma patients to develop an interpretable classification model utilising various preoperative imaging sequences, including T1‐weighted, T2‐weighted, fluid‐attenuated inversion recovery, contrast‐enhanced T1‐weighted, and diffusion‐weighted imaging. The model employs a multi‐task learning framework to extract and fuse radiomic, deep learning, and clinical features for IDH genotyping and glioma prognosis. Additionally, a multi‐modal transformer strategy is integrated to analyse structural and functional MRI, thereby enhancing model performance. Experimental results indicate that the model demonstrates superior performance, surpassing previous research and other state‐of‐the‐art methods. The model achieves an AUC of 91.40% in the IDH genotyping task and 93.37% in the glioma prognosis task. Group analysis reveals that the model exhibits higher sensitivity to IDH‐mutant cases and more accurately identifies low‐risk groups compared to medium‐ or high‐risk groups. This study aims to achieve accurate IDH genotyping and glioma prognosis through effective classification method, offering valuable diagnostic insights for clinical practice and expediting treatment decisions.