Carman-Esparza Cora M, Stine Caleb A, Atay Naciye, Kingsmore Kathryn M, Wang Maosen, Woodall Ryan T, Rockne Russell C, Cunningham Jessica J, Munson Jennifer M
bioRxiv. 2025 Mar 14:2025.03.12.642840. doi: 10.1101/2025.03.12.642840.
Glioblastoma is characterized by aggressive infiltration into surrounding brain tissue, hindering complete surgical resection and contributing to poor patient outcomes. Identifying tumor-specific invasion patterns is essential for advancing our understanding of glioblastoma progression and improving surgical and radiotherapeutic strategies. Here, we leverage in vivo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively quantify interstitial fluid velocity, direction, and diffusion within and around glioblastomas. We introduce a novel vector-based pathline analysis to trace downstream accumulation of fluid flow originating from the tumor core, providing a spatially explicit perspective on local flow patterns. We find that localized fluid transport metrics predict glioblastoma invasion and progression, offering a new framework to non-invasively identify high-risk regions and guide targeted treatment approaches.
Invasion and progression of glioblastoma can be predicted with interstitial fluid flow patterns via magnetic resonance imaging.
胶质母细胞瘤的特征是向周围脑组织进行侵袭性浸润，这阻碍了完全手术切除并导致患者预后不良。识别肿瘤特异性侵袭模式对于增进我们对胶质母细胞瘤进展的理解以及改善手术和放射治疗策略至关重要。在此，我们利用体内动态对比增强磁共振成像（DCE-MRI）来无创地量化胶质母细胞瘤内部及周围的组织液速度、方向和扩散。我们引入了一种基于向量的新型迹线分析方法，以追踪源自肿瘤核心的流体流动的下游积聚情况，从而提供关于局部流动模式的空间明确视角。我们发现局部流体传输指标可预测胶质母细胞瘤的侵袭和进展，为无创识别高风险区域并指导靶向治疗方法提供了一个新框架。
通过磁共振成像利用组织液流动模式可预测胶质母细胞瘤的侵袭和进展。
