Xu Qian, Liang Feng Ning, Cao Ya Ru, Duan Jin, Cui Teng, Zhao Teng, Zhu Hong
Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Front Neurol. 2025 Jul 18;16:1609594. doi: 10.3389/fneur.2025.1609594. eCollection 2025.
Glioma is the most common primary malignant tumor of the central nervous system. The mutation status of isocitrate dehydrogenase (IDH) and the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter are key biomarkers for glioma diagnosis and prognosis. Accurate, non-invasive prediction of these biomarkers using MRI is of significant clinical value.
We proposed a novel multitask deep learning framework based on Coordinate Attention-EfficientNetV2 (CA-EfficientNetV2) to simultaneously predict IDH mutation and MGMT promoter methylation status based on MRI data. Initially, unlabeled MR images were annotated using K-means clustering to generate pseudolabels, which were subsequently refined using a Vision Transformer (ViT) network to improve labeling accuracy. Then, the Fruit Fly Optimization Algorithm (FOA) was employed to assign optimal weights to the pseudolabeled data. The CA-EfficientNetV2 model, integrated with a coordinate attention mechanism, was constructed. The multitask framework comprised three independent subnetworks: T2-net (based on T2-weighted imaging), T1C-net (based on contrast-enhanced T1-weighted imaging), and TU-net (based on the fusion of T2WI and T1CWI).
The proposed framework demonstrated high performance in predicting both IDH mutation and MGMT promoter methylation status. Among the three subnetworks, TU-net achieved the best results, with accuracies of 0.9598 for IDH and 0.9269 for MGMT, and AUCs of 0.9930 and 0.9584, respectively. Comparative analysis showed that our proposed model outperformed other convolutional neural network (CNN) - based approaches.
The CA-EfficientNetV2-based multitask framework offers a robust, non-invasive method for preoperative prediction of glioma molecular markers. This approach holds strong potential to support clinical decision-making and personalized treatment planning in glioma management.
胶质瘤是中枢神经系统最常见的原发性恶性肿瘤。异柠檬酸脱氢酶（IDH）的突变状态和O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子的甲基化状态是胶质瘤诊断和预后的关键生物标志物。利用磁共振成像（MRI）准确、无创地预测这些生物标志物具有重要的临床价值。
我们提出了一种基于坐标注意力-高效网络V2（CA-EfficientNetV2）的新型多任务深度学习框架，以基于MRI数据同时预测IDH突变和MGMT启动子甲基化状态。首先，使用K均值聚类对未标记的MR图像进行标注以生成伪标签，随后使用视觉Transformer（ViT）网络对其进行优化以提高标注准确性。然后，采用果蝇优化算法（FOA）为伪标记数据分配最佳权重。构建了集成坐标注意力机制的CA-EfficientNetV2模型。多任务框架由三个独立的子网组成：T2-net（基于T2加权成像）、T1C-net（基于对比增强T1加权成像）和TU-net（基于T2WI和T1CWI的融合）。
所提出的框架在预测IDH突变和MGMT启动子甲基化状态方面均表现出高性能。在三个子网中，TU-net取得了最佳结果，IDH的准确率为0.9598，MGMT的准确率为0.9269，AUC分别为0.9930和0.9584。对比分析表明，我们提出的模型优于其他基于卷积神经网络（CNN）的方法。
基于CA-EfficientNetV2的多任务框架为术前预测胶质瘤分子标志物提供了一种强大的无创方法。这种方法在支持胶质瘤管理中的临床决策和个性化治疗规划方面具有巨大潜力。
