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
Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA USA.
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA USA.
NPJ Biomed Innov. 2025;2(1):30. doi: 10.1038/s44385-025-00033-x. Epub 2025 Sep 3.
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.
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其周围的间质液流速、方向和扩散情况。我们引入了一种基于向量的新型流线分析方法，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。
