Innovative Algorithm Uses Fluid Dynamics to Predict Glioblastoma Spread

Glioblastoma, one of the most aggressive forms of brain cancer, remains a significant challenge for medical professionals. Conventional treatments such as surgical removal and radiation therapy offer limited extension of survival, primarily because the cancer often hides infiltrative tumor cells in the surrounding tissue, making complete eradication difficult. Patients diagnosed with glioblastoma typically have an average survival time of about 15 months.

Recent research led by Jennifer Munson at the Fralin Biomedical Research Institute at Virginia Tech proposes a novel approach to address this issue. Her team has developed an advanced diagnostic tool that leverages magnetic resonance imaging (MRI), detailed knowledge of how fluid moves through human tissues, and a new algorithm designed to identify and forecast the likely locations of migratory tumor cells.

"Detecting the tumor cells that have migrated away from the primary mass is crucial because without a clear understanding of their whereabouts, treatment efforts might miss these infiltrative cells," explains Munson. "Our method aims to fill this gap by mapping out potential pathways of cellular invasion."

Traditionally, surgical planning for glioblastoma involves radiological scans that depict the tumor's edge but do not reveal the full extent of invasive cells deep within the tissue. Fluorescent dyes can highlight cancer cells during surgery, yet their limited penetration restricts visualization. The new fluid flow-based model simulates how tumor cells follow the paths established by interstitial fluid movement, effectively predicting regions at higher risk of invasion.

Munson's research shows that faster fluid flows tend to correlate with increased tumor cell invasion, whereas more random diffusion is less associated with infiltration. The key innovation is a metric based on how fluid streams merge and diverge—these potential pathways indicate where cancer cells are most likely to migrate.

This predictive capability could enable surgeons to target more invasive regions with greater confidence and precision, reducing residual tumor tissue and potentially improving patient outcomes. Furthermore, the research has inspired the formation of Cairina, a startup dedicated to translating these findings into practical tools for personalized cancer treatment. Cairina aims to produce probability and hotspot maps that guide surgical and radiation interventions, optimizing therapy while sparing healthy tissue.

"Our ultimate goal is to give clinicians detailed maps that highlight invasion-prone areas, allowing for more tailored and effective treatment plans," Munson states. This approach not only improves the chances of removing more invasive tumor cells but also opens new avenues for monitoring tumor progression using fluid flow dynamics.

Munson emphasizes that her work primarily focuses on interstitial fluid flow—the movement of fluid through spaces between cells—which behaves differently depending on the disease context. In glioblastoma, her team found that analyzing fluid flow dynamics, especially the speed and patterns of flow around the tumor, offers vital insights into the pathways of tumor cell migration. The developed metric captures these flow patterns, providing a powerful predictor for invasion risk.

This research represents a significant leap forward in understanding and predicting glioblastoma progression, offering hope for more effective surgeries and targeted therapies in the future.