磁共振成像2025，Vol.16Issue(9)：46-52,59,8.DOI:10.12015/issn.1674-8034.2025.09.008
基于瘤内及瘤周水肿的多参数MRI影像组学-transformer深度学习特征联合模型预测较低级别胶质瘤IDH-1突变状态
Prediction of lower-grade glioma IDH-1 mutation status using a combined model of radiomics and transformer deep learning features based on multi-parametric MRI of intratumoral and peritumoral edema
摘要
Abstract
Objective:To develop a combined model based on multiparametric MRI,radiomics,and deep learning techniques to predict isocitrate dehydrogenase gene(IDH-1)mutation status with lower-grade gliomas(LGGs)in patients.Materials and Methods:Clinical,imaging,and pathological data were retrospectively collected from patients with pathologically confirmed LGGs.Based on multiparametric MRI,a predictive model for IDH-1 mutation status was constructed by combining radiomic features and deep learning features extracted from the 2.5D-CrossFormer deep learning model.Through feature selection,application of machine learning algorithms,and integration with clinical variables,a clinical-radiomics-deep learning nomogram model was developed.Results:A total of 186 patients were included,with 79 IDH-1-positive cases and 107 IDH-1-negative cases.A total of 10 530 radiomic features and 32 deep learning features were extracted.After screening and feature dimensionality reduction,20 radiomics-deep learning features were retained.Among various machine learning models,the LightGBM-based deep radiomics model performed best,with an area under the curve(AUC)of 0.94 in the training group and 0.86 in the validation group.The nomogram model constructed by combining clinical variables achieved an AUC of 0.97 in the training group,significantly outperforming the radiomics model and clinical model,and also demonstrated good predictive performance in the validation group.Conclusions:Based on multiparametric MRI,radiomics,and deep learning techniques,this study successfully constructed a combined model incorporating intratumoral and peritumoral edema features to predict the IDH-1 mutation status in LGGs.This model exhibits high diagnostic accuracy and has the potential to provide important imaging evidence for the formulation of treatment plans and prognosis assessment in LGGs patients.关键词
胶质瘤/异柠檬酸脱氢酶基因突变/磁共振成像/深度学习/影像组学Key words
glioma/isocitrate dehydrogenase gene mutation/magnetic resonance imaging/deep learning/radiomics分类
医药卫生引用本文复制引用
窦越,刘原庆,李勇珺..基于瘤内及瘤周水肿的多参数MRI影像组学-transformer深度学习特征联合模型预测较低级别胶质瘤IDH-1突变状态[J].磁共振成像,2025,16(9):46-52,59,8.基金项目
Suzhou Key Laboratory of Medical Imaging(No.SZS2024032). 苏州市影像医学重点实验室项目(编号:SZS2024032) （No.SZS2024032）
