标题： FoundBioNet：一种基于基础模型的多参数MRI胶质瘤IDH基因分型方法 显示英文标题

标题： FoundBioNet: A Foundation-Based Model for IDH Genotyping of Glioma from Multi-Parametric MRI

摘要： 准确的、非侵入性的异柠檬酸脱氢酶（IDH）突变检测对于有效的胶质瘤管理至关重要。传统的检测方法依赖于侵入性的组织采样，这可能无法捕捉肿瘤的空间异质性。虽然深度学习模型在分子表征方面显示出潜力，但其性能通常受到标注数据稀缺的限制。相比之下，基础深度学习模型为胶质瘤影像生物标志物提供了一种更具泛化性的方法。我们提出了一种基于基础模型的生物标志物网络（FoundBioNet），该网络采用SWIN-UNETR架构，从多参数MRI中非侵入性地预测IDH突变状态。两个关键模块被整合：肿瘤感知特征编码（TAFE），用于提取多尺度、肿瘤聚焦特征；跨模态差异（CMD），用于突出与IDH突变相关的细微T2-FLAIR不匹配信号。该模型在一个来自六个公开数据集的1705名胶质瘤患者的多样化多中心队列上进行了训练和验证。我们的模型在EGD、TCGA、Ivy GAP、RHUH和UPenn的独立测试集上分别达到了90.58%、88.08%、65.41%和80.31%的AUC值，始终优于基线方法（p <= 0.05）。消融研究证实，TAFE和CMD模块对于提高预测准确性都是必不可少的。通过集成大规模预训练和任务特定微调，FoundBioNet实现了泛化的胶质瘤表征。这种方法提高了诊断准确性和可解释性，有望实现更个性化的患者护理。 显示英文摘要

摘要： Accurate, noninvasive detection of isocitrate dehydrogenase (IDH) mutation is essential for effective glioma management. Traditional methods rely on invasive tissue sampling, which may fail to capture a tumor's spatial heterogeneity. While deep learning models have shown promise in molecular profiling, their performance is often limited by scarce annotated data. In contrast, foundation deep learning models offer a more generalizable approach for glioma imaging biomarkers. We propose a Foundation-based Biomarker Network (FoundBioNet) that utilizes a SWIN-UNETR-based architecture to noninvasively predict IDH mutation status from multi-parametric MRI. Two key modules are incorporated: Tumor-Aware Feature Encoding (TAFE) for extracting multi-scale, tumor-focused features, and Cross-Modality Differential (CMD) for highlighting subtle T2-FLAIR mismatch signals associated with IDH mutation. The model was trained and validated on a diverse, multi-center cohort of 1705 glioma patients from six public datasets. Our model achieved AUCs of 90.58%, 88.08%, 65.41%, and 80.31% on independent test sets from EGD, TCGA, Ivy GAP, RHUH, and UPenn, consistently outperforming baseline approaches (p <= 0.05). Ablation studies confirmed that both the TAFE and CMD modules are essential for improving predictive accuracy. By integrating large-scale pretraining and task-specific fine-tuning, FoundBioNet enables generalizable glioma characterization. This approach enhances diagnostic accuracy and interpretability, with the potential to enable more personalized patient care.