评估脑体积信息在多发性硬化症进展中的作用
Assessing the Role of Volumetric Brain Information in Multiple Sclerosis Progression
AI总结：
本研究利用复发性多发性硬化症临床试验的体素级脑MRI数据，扩展模型无关特征重要性度量，识别出与临床进展对应且具预测性的脑区特征，结合生存模型和3D卷积神经网络验证了其预后强度与临床相关性。
摘要：
多发性硬化症是一种影响中枢神经系统的慢性自身免疫性疾病。理解多发性硬化症的进展并识别相关的大脑结构对于个性化治疗决策至关重要。基于形变的形态学测量利用解剖磁共振成像在体素水平定量评估大脑体积变化，从而深入了解每个大脑区域在神经变性方面对临床进展的贡献。利用来自复发性多发性硬化症临床试验的此类体素级数据，我们扩展了一种模型无关的特征重要性度量，以识别与临床进展相对应的稳健且具有预测性的特征集。这些特征对应于在多发性硬化症疾病研究中具有临床意义的大脑区域，证明了它们的科学相关性。当使用经典生存模型和3D卷积神经网络预测进展时，所识别的区域产生了性能最佳的模型，证明了它们的预后强度。我们还发现，这些特征能很好地推广到临床进展的其他定义，并能弥补高度预后临床特征的缺失，强调了基于形变的形态学测量作为区域识别工具的预测能力和临床相关性。
英文摘要：
Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes at the voxel level, providing insight into how each brain region contributes to clinical progression with regards to neurodegeneration. Utilizing such voxel-level data from a relapsing multiple sclerosis clinical trial, we extend a model-agnostic feature importance metric to identify a robust and predictive feature set that corresponds to clinical progression. These features correspond to brain regions that are clinically meaningful in MS disease research, demonstrating their scientific relevance. When used to predict progression using classical survival models and 3D convolutional neural networks, the identified regions led to the best-performing models, demonstrating their prognostic strength. We also find that these features generalize well to other definitions of clinical progression and can compensate for the omission of highly prognostic clinical features, underscoring the predictive power and clinical relevance of deformation-based morphometry as a regional identification tool.
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