Glioblastoma, an aggressive and relentless form of brain cancer, remains one of the most daunting challenges in oncology. Despite surgical resection and radiotherapy, the prognosis for patients diagnosed with glioblastoma remains grim, with average survival times barely extending beyond 15 months. Traditional interventions fall short primarily because glioblastoma cells infiltrate surrounding brain tissue with stealth, evading detection and sparking tumor recurrence. The hidden nature of these migrating cells demands innovative strategies not only to locate them but also to anticipate the tumor’s future progression locations within the brain.
A team led by Dr. Jennifer Munson at the Fralin Biomedical Research Institute at Virginia Tech Carilion has pioneered a groundbreaking approach that leverages the intricate dynamics of fluid flow within brain tissue to predict glioblastoma invasion. Their research integrates advanced magnetic resonance imaging (MRI) with detailed knowledge of interstitial fluid mechanics—the movement of fluid between cells—paired with cutting-edge algorithms to map pathways cancer cells might exploit to migrate beyond visible tumor borders. This fusion of biomedical engineering and cancer biology holds promise to revolutionize how neurosurgeons and oncologists strategize treatments.
The central tenet of Munson’s work rests on the observation that interstitial fluid flow within tissues is not random but follows distinct trajectories influenced by the structure and physiology of the microenvironment. In glioblastoma, these fluid currents appear to serve as highways that facilitate tumor cell invasion into adjacent healthy brain regions. By using MRI to capture the subtle changes and patterns of this fluid movement, Munson’s team has been able to generate predictive models that reveal where cancer cells are most likely to infiltrate next, advancing beyond the limitations of standard imaging techniques and intraoperative fluorescence that depend on visible tumor markers.
Unlike conventional radiological assessments that merely identify the margin of the bulk tumor mass, this new methodology exposes a hidden network of “fluid pathlines” emanating from the tumor core. These pathlines, visualized in striking blue hues in detailed imaging, represent converging and diverging flows that correspond closely to zones of invasive cell dispersal. By quantifying characteristics of these streams—such as their velocity, directionality, and diffusion properties—the researchers developed a novel metric that outperforms existing predictors of tumor spread. Their findings highlight that increased flow velocity correlates with enhanced tumor invasion, whereas more diffusive, randomized fluid movement is associated with restrained cellular spread.
This nuanced picture of the tumor microenvironment allows for a more sophisticated stratification of brain tissue surrounding the glioblastoma. Surgeons could, therefore, tailor their resections more aggressively in regions flagged by the predictive models while sparing healthy tissue where invasion risk is minimal. This precision not only optimizes tumor clearance but also mitigates damage to critical brain functions, balancing effectiveness with patient quality of life.
Crucially, Munson’s research posits that cancer cells are not merely passive entities migrating randomly but may exploit the physical forces exerted by interstitial fluid flow to navigate the mechanical landscape of brain tissue. This interplay between biomechanical forces and cellular behavior underscores a developing paradigm in cancer biology that appreciates tumors as integrated systems influenced by physics, rather than isolated clusters of rogue cells.
The translation of these findings from bench to bedside is already underway through Cairina Inc., a spin-off enterprise co-founded by Munson and colleagues. Cairina plans to commercialize these predictive maps as actionable tools for clinicians—providing probability or “hotspot” maps of tumor cell invasion that could guide surgical planning, radiotherapy dosing, and systemic therapies. This personalized approach promises to elevate glioblastoma treatment from reactive to proactive, potentially improving outcomes for patients who currently face a dire prognosis.
However, the technical challenges remain significant. MRI must achieve sufficiently high resolution and sensitivity to detect subtle tissue fluid movements, while computational models need continuous refinement to capture the complexity of individual tumor environments accurately. Additionally, integrating these tools seamlessly into clinical workflows requires collaboration across multidisciplinary teams, from imaging specialists and neurosurgeons to data scientists.
Moreover, this interstitial fluid flow-based metric may extend beyond glioblastoma, offering insights into other invasive cancers and neurological disorders characterized by altered tissue mechanics and fluid dynamics. By harnessing the principles of classical mechanics, particularly fluid dynamics, to interpret biological phenomena, this research bridges physics and medicine in a novel manner with broad implications.
The funding supporting this transformative work comes from esteemed organizations such as the National Cancer Institute, the Red Gates Foundation, the American Cancer Society, and the National Institute of Neurological Disorders and Stroke. Their backing underscores the critical importance and high potential impact of this research direction in combating some of the most lethal brain cancers.
In summary, by revealing the concealed highways along which glioblastoma cells travel, Dr. Munson and her team are not only decoding the physical language of tumor invasion but are equipping the medical community with unprecedented predictive power. This approach holds the potential to shift the paradigm of glioblastoma treatment, moving from a blunt fight against visible tumors to a smart, fluid dynamics-informed campaign against the unseen invaders lurking just beneath the surface.
Subject of Research: Cells
Article Title: Interstitial fluid transport dynamics predict glioblastoma invasion and progression
News Publication Date: 3-Sep-2025
Web References:
https://www.nature.com/articles/s44385-025-00033-x
https://fbri.vtc.vt.edu/people-directory/primary-faculty/munson.html
https://fbri.vtc.vt.edu/
https://cairinainc.com/
References:
Munson, J., Rockne, R., Stine, A., Cunningham, R., & Woodall, B. Interstitial fluid transport dynamics predict glioblastoma invasion and progression. npj Biomedical Innovations (2025). DOI: 10.1038/s44385-025-00033-x
Image Credits: Jennifer Munson/Virginia Tech
Keywords: Brain cancer, Glioblastomas, Metastasis, Neurological disorders, Fluid flow
Tags: advanced algorithms in cancer predictionbiomedical engineering in oncologyDr. Jennifer Munson research findingsglioblastoma brain cancer treatment advancesinnovative strategies in oncologyinterstitial fluid dynamics in tumorsmapping cancer cell migration pathwaysneurosurgery and glioblastomaprecision medicine for glioblastomaprediction of glioblastoma invasionrole of MRI in cancer researchtumor recurrence challenges
