The most typical form of malignant brain cancer may be easier to identify according to research from the University of Bristol.
The creation of an easy blood test for the detection of glioblastomas (GBMs) may lead to earlier diagnosis and more efficient, individualised treatment choices.
The Bristol-led study, which was published in the Journal of the Royal Society Interface, includes the creation of mathematical models to evaluate the use of biomarkers currently being made in the identification of GBMs and the potential for improvement of such biomarker-based approaches.
This study is a component of a larger CRUK initiative run by the University of Bristol to provide an accessible point-of-care blood test for the detection of brain tumours. This interdisciplinary effort integrates computational modelling, fluorescent nanoparticle synthesis, and innovative testing methodologies with biomarker identification.
This recent study established mathematical models and combined them with experimental data. The scientists discovered that reducing the existing biomarker threshold for the potential GBM biomarker glial fibrillary acidic protein (GFAP) could result in early detection of GBMs. The team also employed computer modelling to investigate how patient and tumour variables affected detection and improvement tactics.
“Our findings provide the basis for further clinical data on the impact of lowering the current detection threshold for the known biomarker, GFAP, to allow earlier detection of GBMs using blood tests,” said Dr. Johanna Blee, lead author and Research Associate at the Department of Engineering Mathematics at the University of Bristol. It might be able to quantify patient and tumour heterogeneities and factor errors into our models and forecasts for blood levels for various malignancies with more experimental data. We have also shown how our models may be used in conjunction with other diagnostics, such as scans, to improve clinical insight and create more individualised and efficient therapies.
These mathematical models could be used to evaluate and contrast newly developed brain tumour diagnostic procedures and biomarkers. We are optimistic that this research may eventually contribute to the creation of a straightforward blood test for brain cancers, enabling earlier and more accurate diagnosis.