MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas

作者：Wenju Niu, Junyu Yan, Min Hao, Yibo Zhang, Tianshi Li, Chen Liu, Qijian Li, Zihao Liu, Yizi Su, Bo Peng, Yan Tan, Xiaochun Wang, Lei Wang, Hui Zhang, Guoqiang Yang · 发表于：npj Precision Oncology · 年份：2025 · DOI：10.1038/s41698-025-00884-y · 被引用次数：25 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Cancer, Hypoxia, and Metabolism

This study aims to predict IDH wt with TERTp-mut gliomas using multiparametric MRI sequences through a novel fusion model, while matching model classification metrics with patient risk stratification aids in crafting personalized diagnostic and prognosis evaluations.Preoperative T1CE and T2FLAIR sequences from 1185 glioma patients were analyzed. A MultiChannel_2.5D_DL model and a 2D DL model, both based on the cross-scale attention vision transformer (CrossFormer) neural network, along with a Radiomics model, were developed. These were integrated via ensemble learning into a stacking model. The MultiChannel_2.5D_DL model outperformed the 2D_DL and Radiomics models, with AUCs of 0.806-0.870. The stacking model achieved the highest AUC (0.855-0.904) across validation sets. Patients were stratified into high-risk and low-risk groups based on stacking model scores, with significant survival differences observed via Kaplan-Meier analysis and log-rank tests. The stacking model effectively identifies IDH wt TERTp-mutant gliomas and stratifies patient risk, aiding personalized prognosis.