The study also illustrates the key role of mathematical modeling and artificial intelligence in the development and implementation of blood-based liquid biopsy. Here, its application centers on the development of a simple blood test to detect GBMs and enable earlier diagnosis and more effective and personalized treatment options. Finally, the research is part of a larger project at the University of Bristol concerning the diagnosis of brain tumors and which combines the discovery of biomarkers, the development of fluorescent nanoparticles and new testing techniques with computer modelling.
Here, the mathematical model developed from patient data confirms the interest
of a new prospective GBM biomarker, glial fibrillary acidic protein (GFAP).
- Lowering the level of the biomarker below a certain threshold could predict the development of GBM. Then, the modeling explores the impact of tumor characteristics and some patient-specific data on detection and treatment strategy.
Dr Johanna Blee, lead author and researcher in the Department of Engineering at the University of Bristol explains that with other data it is indeed possible to quantify tumor heterogeneities. Finally, combined with other types of diagnostics such as CT scans, the liquid biopsy model can make it possible to offer the patient a more personalized and therefore more effective treatment.
“This research will ultimately contribute to the development of a simple blood test for brain tumors, allowing earlier and more detailed diagnoses.”