MRI放射组学的多视图范式转变：预测胶质瘤MGMT甲基化
The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma
AI总结：
本文提出基于变分自编码器的多视图潜在表示学习框架，通过整合互补的MRI信息，显著提升了MGMT启动子甲基化预测性能。
摘要：
非侵入性地从医学影像推断肿瘤分子特征是放射组学的核心目标，特别是在胶质瘤中，O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子甲基化具有重要的预后和治疗意义。尽管基于放射组学的机器学习方法已显示出潜力，但传统单模态和早期融合方法常受限于高特征冗余和对模态特定信息建模不完整。在本工作中，我们引入了基于变分自编码器（VAE）的多视图潜在表示学习框架，该框架在保留模态特定放射组学结构的同时，能够在紧凑的概率潜在空间中实现晚期融合。该方法在从增强T1加权（T1Gd）和流体衰减反转恢复（FLAIR）磁共振成像（MRI）中提取的坏死肿瘤核心的放射组学特征上进行了评估。实验结果表明，所提出的多视图VAE结合随机森林分类器在测试面积下接收者操作特征（ROC）曲线（AUC）为0.77（95%置信区间：0.71-0.83），显著优于基线放射组学模型（AUC=0.54）和超参数调优模型（AUC=0.64）。这些发现表明，多视图概率编码能够更有效地整合互补的MRI信息，并显著提高MGMT启动子甲基化状态的预测性能。
英文摘要：
Non-invasive inference of molecular tumor characteristics from medical imaging is a central goal of radiogenomics, particularly in glioblastoma (GBM), where O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation carries important prognostic and therapeutic significance. Although radiomics-based machine learning methods have shown promise for this task, conventional unimodal and early-fusion approaches are often limited by high feature redundancy and incomplete modeling of modality-specific information. In this work, we introduce a multi-view latent representation learning framework based on variational autoencoders (VAE) that preserves modality-specific radiomic structure while enabling late fusion in a compact probabilistic latent space. The approach is evaluated on radiomic features extracted from the necrotic tumor core in post-contrast T1-weighted (T1Gd) and Fluid-Attenuated Inversion Re-covery (FLAIR) Magnetic Resonance Imaging (MRI). Experimental results demonstrate that the proposed multi-view VAE combined with a random forest classifier achieves a test Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.77 (95% confidence interval: 0.71-0.83), substantially outperforming both a baseline radiomics model (AUC = 0.54) and a hyperparameter-tuned model (AUC = 0.64). These findings indicate that multi-view probabilistic encoding enables more effective integration of complementary MRI information and significantly improves predictive performance for MGMT promoter methylation status.
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