The standard full blood count (FBC) is a common test used to diagnose a range of blood disorders, including anaemia, genetic blood disorders, and cancer. However, in most cases, the FBC is just a starting point and further tests are needed for a definitive diagnosis. These additional tests, like examining blood films or performing molecular assays, are not available everywhere.
Only about 5% of abnormal FBC tests result in a blood film being made. This film can provide information about infections, iron deficiency anaemia, platelet disorders, and blood cancers. Unfortunately, there is a shortage of experts who can analyse these films, which creates a challenge for healthcare providers. We are investigating the use of rich full blood count (R-FBC) data, which is a larger set of data produced by blood analysers. If we can find that computerised analysis of R-FBC data is as good as traditional methods, it would ease the burden on healthcare staff.
It's likely that many blood disorders, especially in their early stages, cause subtle changes in blood parameters that can be detected through R-FBC analysis, but not routine testing. This could lead to earlier diagnoses and more effective treatment. Additionally, there is evidence that certain types of blood cancer cause specific changes in the appearance of blood cells that can only be detected through computational analysis. Furthermore, we now know that certain blood cell abnormalities can appear years before the onset of certain blood cancers. The information obtained from routine FBC testing, particularly the red cell distribution width, has only limited predictive value. We hypothesise that the detailed information provided by R-FBC analysis could improve the accuracy of predictive models for a wider range of blood disorders.
We are investigating how R-FBC data can help distinguish between healthy and diseased blood production, focusing initially on blood cancers. We will also explore how this data can predict the onset of blood disorders before they manifest and categorise patients based on specific characteristics, such as molecular features and prognosis. The results from these studies can be integrated into routine blood tests, benefiting screening, diagnosis, and treatment decisions for patients.