磁共振成像2026，Vol.17Issue(8)：154-160,183,8.DOI:10.12015/issn.1674-8034.2026.08.017
多模态影像组学与深度学习无创预测胶质瘤TERT启动子状态及辅助临床决策的研究进展
Advances in multimodal radiomics and deep learning for predicting TERT promoter status and assisting clinical decision-making in glioma
摘要
Abstract
Telomerase reverse transcriptase promoter mutation is a core molecular event in the molecular classification of gliomas.Detecting these mutations provides crucial guidance for patient prognosis assessment and the development of individualized treatment plans.Traditional detection methods rely on invasive tissue biopsy,which has inherent limitations such as tumor spatiotemporal heterogeneity,sampling bias,and the inability to perform dynamic monitoring,making it difficult to meet the clinical needs of precise diagnosis and treatment.In recent years,radiomics based on multiparametric MRI and deep learning technology have developed rapidly and have become an important research direction for non-invasive preoperative prediction of TERT promoter mutation status in gliomas.Research shows that some multimodal fusion models demonstrate certain generalization ability in limited external validation,but performance degradation across centers remains a common challenge.Meanwhile,the translation of this technology into routine clinical practice still faces key challenges,including image standardization,cross-center data sharing,and insufficient model interpretability.Looking ahead,by promoting large-scale prospective clinical trials,establishing standardized cross-center imaging databases,integrating spatial multi-omics and liquid biopsy cross-modal information,and optimizing models with technologies such as federated learning and explainable AI,it is hoped that imaging AI prediction models can be transformed into reliable clinical decision-support tools,advancing intelligent and individualized glioma diagnosis and treatment.This review systematically summarizes the latest research progress in this field,focusing on the methodological construction,model performance optimization,and clinical translation value of multi-modal radiomics and deep learning models in predicting TERT promoter mutations in gliomas.It also analyzes core development features in current research,such as model refinement,multi-modal fusion,and interpretability exploration,aiming to provide methodological reference for non-invasive preoperative prediction of TERT promoter mutations in gliomas.关键词
胶质瘤/端粒酶逆转录酶启动子突变/影像组学/深度学习/磁共振成像/术前预测Key words
glioma/telomerase reverse transcriptase promoter mutation/radiomics/deep learning/magnetic resonance imaging/preoperative prediction分类
医药卫生引用本文复制引用
杨舵,李丽鑫,延雨欣,王晓彤,蒋盈盈,白玉萍..多模态影像组学与深度学习无创预测胶质瘤TERT启动子状态及辅助临床决策的研究进展[J].磁共振成像,2026,17(8):154-160,183,8.基金项目
Cuiying Scholars Research Cultivation Program Project of Lanzhou University Second Hospital(No.CXYZ2022-43,CYXZPT2026-11) （No.CXYZ2022-43,CYXZPT2026-11）
Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital(No.CY2024-LC-B02). 兰州大学第二医院萃英学子科研培育计划项目(编号:CXYZ2022-43、CYXZPT2026-11) （No.CY2024-LC-B02）
兰州大学第二医院萃英科技创新计划项目(编号:CY2024-LC-B02) （编号:CY2024-LC-B02）
