Li Darui, Hu Wanjun, Ma Laiyang, Yang Wenxia, Liu Yang, Zou Jie, Ge Xin, Han Yuping, Gan Tiejun, Cheng Dan, Ai Kai, Liu Guangyao, Zhang Jing
Department of Nuclear Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China.
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Magn Reson Imaging. 2025 Apr;117:110314. doi: 10.1016/j.mri.2024.110314. Epub 2024 Dec 19.
To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.
A total of 402 glioma patients from two independent centers were retrospectively included, and the data from center I was randomly divided into a training cohort (n = 239) and an internal validation cohort (n = 103) on a 7:3 basis. Center II served as an independent external validation cohort (n = 60). We developed a DLRN for IDH classification of gliomas based on T2 images. This hybrid model integrates deep learning features, radiomics features, and clinical features most relevant to IDH genotypes and finally classifies them using multivariate logistic regression analysis. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the performance of the model and applied the DLRN score to the survival analysis of some of the follow-up glioma patients.
The proposed model had an area under the curve (AUC) of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients.
Deep learning radiomics nomograms performed well in non-invasively predicting IDH mutation status in gliomas, assisting stratified management and targeted therapy for glioma patients.
探讨深度学习影像组学列线图（DLRN）预测异柠檬酸脱氢酶（IDH）基因型的可行性。
回顾性纳入来自两个独立中心的402例胶质瘤患者，中心I的数据按7:3随机分为训练队列（n = 239）和内部验证队列（n = 103）。中心II作为独立的外部验证队列（n = 60）。我们基于T2图像开发了一种用于胶质瘤IDH分类的DLRN。这种混合模型整合了深度学习特征、影像组学特征以及与IDH基因型最相关的临床特征，最后使用多变量逻辑回归分析对其进行分类。我们使用受试者操作特征（ROC）曲线下面积（AUC）来评估模型的性能，并将DLRN评分应用于部分随访胶质瘤患者的生存分析。
在外部验证队列中，所提出的模型曲线下面积（AUC）为0.98，并且DLRN评分与胶质瘤患者的总生存期显著相关。
深度学习影像组学列线图在无创预测胶质瘤IDH突变状态方面表现良好，有助于胶质瘤患者的分层管理和靶向治疗。
