Machine learning for improving blood cancer diagnostics via flow cytometry

The integrated Haemato-oncology diagnostics service (HODS) based at the Royal Liverpool Hospital performs testing for the Merseyside and Cheshire Cancer Network for blood cancers including Lymphoma, Myeloma and Acute Leukaemia. One of tests performed is to use flow cytometry to examine the cells within a cell suspension prepared from blood, bone marrow or other body fluid e.g. CSF. Flow cytometry is performed in multi-channel mode (e.g. 8 or 12 channels) where each channel reports a different property: size, shape, intensity of fluorochrome expression (a surrogate for presence/level of protein expression on the cell surface via antibody binding etc) of each cell in the population.
Present practice at HODS is for manual examination of data, for example via visualisation of dual parameter dot-plots of different properties for cells, to both determine the immunophenotype of the cellular populations and enumerate any abnormal/malignant sub clones. Populations within the sample are grouped according to their surface antigen expression, with certain patterns of expression corresponding to the different WHO classified lymphomas or leukaemias. An expert analyst then views the plots produced by existing software and writes a report, which is used by the clinician to make a final diagnosis. The current practice is labour intensive and has the potential to miss non-linear or complex associations between phenotypic parameters of cells. In addition identification of low levels of involvement is difficult using conventional methods.
The data is ideally suited to the application of machine learning (e.g. random forest or artificial neural networks), due to the wealth of data stored and labelled in a local database (going back >10 years), and clear potential for such methods to identify subtle patterns in the data not obviously visible to the human eye. A new system is soon to come online in the laboratory, in which rare event detection will be key, for example to identify small counts of cancerous cells (e.g. a few cells per million) following a patient undergoing treatment, such as chemotherapy. There is significant room for innovation in the informatics approach needed to optimise analysis.
The student will be fully trained in machine learning, signal detection and software engineering methods, and will be able to get regular access to the immunophenotyping lab to learn about protocols and procedures, and understand the most critical factors in developing improved diagnostic ability. The project will develop ML methods for identification of cancer types, and rare event detection. They will next develop new software, to visualise data and run the ML models, to be used in HODS, in a complementary manner to current practice.