BTDNet：一种用于脑肿瘤放射基因组学分类的多模态方法
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification
AI总结：
针对脑肿瘤MGMT启动子甲基化状态检测传统方法低效的问题，提出多模态模型BTDNet，解决体数据长度可变和体级标注难题，在BraTS 2021挑战赛中性能远超现有最优方法，助力脑肿瘤精准诊疗。
摘要：
脑肿瘤在全球范围内带来了严峻的健康挑战，其中胶质母细胞瘤是侵袭性最强的类型之一。准确判定O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子甲基化状态对个性化治疗策略至关重要，但传统方法耗力且耗时。本文提出了一种新型多模态方法BTDNet，利用多参数MRI扫描（包括FLAIR、T1w、T1wCE和T2 3D体数据）预测MGMT启动子甲基化状态。BTDNet解决了两大核心挑战：体数据长度可变（即每个体数据包含的切片数量不同）和体数据级标注（即标注针对整个3D体数据而非其组成的独立切片）。BTDNet由四个模块构成：i）数据增强模块（执行几何变换、数据对凸组合以及测试时数据增强）；ii）3D分析模块（通过CNN-RNN进行全局分析）；iii）路由模块（包含一个掩码层，用于处理可变的输入特征长度）；iv）模态融合模块（有效增强数据表征、减少歧义并缓解数据稀缺问题）。该方法在RSNA-ASNR-MICCAI BraTS 2021挑战赛中大幅优于现有最优方法，为提升脑肿瘤诊断和治疗水平提供了有前景的方向。
英文摘要：
Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. Accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized treatment strategies. However, traditional methods are labor-intensive and time-consuming. This paper proposes a novel multi-modal approach, BTDNet, leveraging multi-parametric MRI scans, including FLAIR, T1w, T1wCE, and T2 3D volumes, to predict MGMT promoter methylation status. BTDNet addresses two main challenges: the variable volume lengths (i.e., each volume consists of a different number of slices) and the volume-level annotations (i.e., the whole 3D volume is annotated and not the independent slices that it consists of). BTDNet consists of four components: i) the data augmentation one (that performs geometric transformations, convex combinations of data pairs and test-time data augmentation); ii) the 3D analysis one (that performs global analysis through a CNN-RNN); iii) the routing one (that contains a mask layer that handles variable input feature lengths), and iv) the modality fusion one (that effectively enhances data representation, reduces ambiguities and mitigates data scarcity). The proposed method outperforms by large margins the state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge, offering a promising avenue for enhancing brain tumor diagnosis and treatment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
