Robbe Fonteyn
Doctoral fellow – Data Mining and Modelling for Biomedicine Group (Saeyslab), Inflammation Research Center – VIB-UGent
Principal investigator: prof. Yvan Saeys (PhD)
Research focus
Measurable residual disease (MRD) is used as an important indicator for the risk of relapse in blood cancers like B-cell acute lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML) and multiple myeloma (MM). One of the most common ways to detect this residual cell population during follow-up blood measurements is flow cytometry. While the technique is fast, inexpensive and widely applicable to many patient groups, it does have a drawback to its analysis. MRD is quantified through manual gating, it is a process that heavily relies on operator expertise, making it subjective and difficult to standardize.
My research focuses on developing an automated detection method for MRD in flow cytometry data of patients with blood cancer. My aim is to build an objective computational pipeline, utilizing machine learning and several preprocessing steps, that can be used in the clinic to support clinicians by making MRD detection faster, more consistent, and less dependent on individual interpretation. This approach has the potential to improve the reliability of relapse-risk assessment and contribute to more consistent treatment decisions.