基于MRI的神经肌肉疾病深度放射组学表型分析：拓扑驱动的表征
MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterization
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中文总结 AI 辅助
本研究提出基于图拓扑等特征的自动化放射组学表型框架，利用1184例MRI扫描开发的拓扑与3D几何不变量，可作为神经肌肉疾病鉴别诊断的生物标志物，相关流程已集成于MUSCAT库。
中文摘要 AI 辅助
肌肉MRI的定量评估对监测神经肌肉疾病（NMD）至关重要。本研究引入一种自动化放射组学表型框架，该框架基于五个主要架构领域设计的原始特征：定量形态测量、空间分布、几何形状、渐进性脂肪替代阶段间的相互作用，以及基于图的拓扑结构。利用CoMPaSS-NMD项目的1184例MRI扫描，我们将异质性肌内脂肪变性的复杂3D结构映射为客观、形态可解释的生物标志物。我们引入脂肪浸润的基于图的骨架化方法以量化肌肉结构变化，通过在整个3D肌肉体积上映射拓扑网络，建立了传统空间无关体积度量的多维扩展。通过非参数Kruskal-Wallis分析的统计筛选证实了这些新型描述符在遗传层级间的判别能力。值得注意的是，拓扑网络度量（如SF1_Skel_Nodes，ε²=0.2656）和界面动态度量（如SF2_To_SF1_Dist_Min，ε²=0.2092）表现出显著效应量，比经典体积评估提供更深入的结构见解。事后配对评估和UMAP投影进一步表明，这些拓扑和3D几何不变量能够捕捉疾病特异性的宏观浸润模式。这些结果表明，全局结构特征是一类极具潜力的鉴别诊断生物标志物，为神经肌肉诊断中追踪纵向疾病动态提供了新途径。所开发的自动化特征提取流程已集成并可在MUSCAT（MUSCle fAt Topology）库中获取。
英文摘要
Quantitative assessment of muscle MRI is crucial for monitoring neuromuscular disorders (NMD). This study introduces an automated radiomic phenotyping framework based on original features engineered across five main architectural domains: quantitative morphometry, spatial distribution, geometric shape, interactions between progressive fat replacement stages, and graph-based topology. Utilizing 1184 MRI scans from the CoMPaSS-NMD project, we map the complex 3D architecture of heterogeneous intramuscular lipodegeneration into objective, morphologically interpretable biomarkers. We introduce a graph-based skeletonization of fat infiltrates to quantify muscle architectural changes, establishing a multi-dimensional extension of traditional, spatially-agnostic volume metrics by mapping topological networks across the entire 3D muscle volume. Statistical screening via non-parametric Kruskal-Wallis analysis confirmed the discriminative power of these novel descriptors across the genetic hierarchy. Notably, topological network metrics (e.g., SF1_Skel_Nodes, $ε^2$ = 0.2656) and interface dynamics metrics (e.g., SF2_To_SF1_Dist_Min, $ε^2$ = 0.2092) demonstrated substantial effect sizes, providing deeper structural insights than classical volumetric assessments. Post-hoc pairwise evaluations and UMAP projections further indicated the capability of these topological and 3D geometric invariants to capture disease-specific macroscopic infiltration patterns. These results demonstrate that global architectural features represent a highly promising class of biomarkers for differential diagnosis, offering new avenues for tracking longitudinal disease dynamics in neuromuscular diagnostics. The developed automated feature extraction pipeline is integrated and available within the MUSCAT (MUSCle fAt Topology) library.
发表机构
- Silesian University of Technology（西里西亚工业大学）
- Newcastle University（纽卡斯尔大学）
- University of Modena and Reggio Emilia（摩德纳与雷焦艾米利亚大学）
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