用MRI影像无创预测脑胶质瘤甲基化状态，提升精准治疗可能。
Explainable Deep Radiogenomic Molecular Imaging for MGMT Methylation Prediction in Glioblastoma
- 融合多模态MRI与深度学习，提取影像特征预测分子标记。
- 在两个数据集上准确率超85%，实现高精度非侵入预测。
- 结合可解释AI技术，让模型决策过程透明可信，适合临床使用。
胶质母细胞瘤（GBM）是一种高度恶性的原发性脑肿瘤，治疗选择有限且预后差。O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）基因启动子的甲基化状态是影响患者对替莫唑胺化疗反应的关键分子生物标志物。传统检测方法依赖有创活检，受限于肿瘤内异质性和操作风险。本研究提出一种放射基因组学分子影像分析框架，基于多参数磁共振成像（mpMRI）实现非侵入性MGMT启动子甲基化状态预测。该方法整合影像组学、深度学习与可解释人工智能（XAI），从FLAIR、T1加权、T1增强及T2加权MRI序列中提取影像特征，同时采用3D卷积神经网络学习深层表型表示。通过早期融合与注意力机制融合互补特征，并进行分类预测。为提升临床可解释性，应用Grad-CAM与SHAP等XAI方法可视化模型决策依据。框架在RSNA-MICCAI Radiogenomic Classification数据集上训练，并在BraTS 2021数据集上外部验证，结果表明其具备高精度、非侵入性与可解释性，推动了人工智能驱动的放射基因组学在精准肿瘤学中的应用。
原文摘要 · Abstract (English)
Glioblastoma (GBM) is a highly aggressive primary brain tumor with limited therapeutic options and poor prognosis. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter is a critical molecular biomarker that influences patient response to temozolomide chemotherapy. Traditional methods for determining MGMT status rely on invasive biopsies and are limited by intratumoral heterogeneity and procedural risks. This study presents a radiogenomic molecular imaging analysis framework for the non-invasive prediction of MGMT promoter methylation using multi-parametric magnetic resonance imaging (mpMRI). Our approach integrates radiomics, deep learning, and explainable artificial intelligence (XAI) to analyze MRI-derived imaging phenotypes and correlate them with molecular labels. Radiomic features are extracted from FLAIR, T1-weighted, T1-contrast-enhanced, and T2-weighted MRI sequences, while a 3D convolutional neural network learns deep representations from the same modalities. These complementary features are fused using both early fusion and attention-based strategies and classified to predict MGMT methylation status. To enhance clinical interpretability, we apply XAI methods such as Grad-CAM and SHAP to visualize and explain model decisions. The proposed framework is trained on the RSNA-MICCAI Radiogenomic Classification dataset and externally validated on the BraTS 2021 dataset. This work advances the field of molecular imaging by demonstrating the potential of AI-driven radiogenomics for precision oncology, supporting non-invasive, accurate, and interpretable prediction of clinically actionable molecular biomarkers in GBM.
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