Utilisation of machine learning algorithms to predict relapse risk following allogeneic stem cell transplantation (alloSCT) for patients with myeloid malignancies

Abstract

Background: Disease relapse remains the primary cause of treatment failure following alloSCT for acute myeloid leukaemia (AML) and myelodysplasia/myeloproliferative neoplasms (MDS/MPN). Early mixed donor chimerism is frequently associated with relapse risk at a population level, yet its predictive accuracy is modest when considered in an individual patient. We hypothesised that a more nuanced analysis of peripheral blood CD3+/CD3- chimerism dynamics in combination with key recipient, donor and transplant-related variables could improve relapse prediction, providing a valuable tool to guide post-alloSCT surveillance and pre-emptive intervention decision making. Methods: We conducted a retros..