PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA

Topic: 3. Acute myeloid leukemia - Biology & Translational Research Background: The detection of measurable residual disease (MRD) in patients with acute myeloid leukemia (AML) during therapy and follow-up is important to identify leukemia persistence or recurrence to individualize treatment strategies. Complementing a range of molecular techniques, multiparametric flow cytometry (MFC) has emerged as an inexpensive and faster method to monitor MRD. However, the manual analysis of such measurements via a sequential, 2-dimensional gating procedure is still time-consuming, rater-dependent and requires a high level of expertise. Aims: We aimed to investigate the applicability of different machine learning (ML) algorithms for MFC-based MRD monitoring to estimate their potential to speed-up and support clinical decision-making. Therefore, we trained different artificial neural networks (ANNs) to automatically quantify MRD levels and analyzed their performance. Methods: In total, 449 MFC measurements of AML patients at diagnosis (n=203), after intensive induction chemotherapy (n=246) and data from 87 leukemia-free controls were used. The eight-colour panel contains the core antigens for MRD detection recommended by ELN. Raw MFC data was utilized either in the format of a 14-dimensional table or a UMAP representation of the same data in form of two-dimensional scatter plot images. The patient data was complemented by a patient and cell-specific classification of MRD status based on a standardized and reproducible MFC MRD approach validated at the end of intensive induction therapy (Röhnert et al., Leukemia 2022). We then trained different ANNs either to automatically identify single cell types and subsequently classify single patients or to predict the individual MRD status based on the aggregated patient data. Finally, we compared the automated classification results with the validated approach and available clinical data (e.g. overall survival). Results: Our results indicate that certain ML-based methods for the classification of MRD achieve prediction accuracies of up to 80% compared to the validated MRD approach. Comparison with respect to clinical outcomes are inherently biased by the necessary treatment adaptions but will serve as an additional reference beyond the manual gating results to further evaluate the potential of the different approaches. Furthermore, we also show that an automated MRD prediction based on single-cell classification mainly suffers from the highly imbalanced nature of the data sets where the frequencies of only small fractions of cells are crucial for the diagnosis. Although we do not achieve a one-to-one correspondence with the manual gating strategy, the ML-based results are obtained much faster and may also be used outside specialized centers to support clinical decision-making processes. Summary/Conclusion: We show the general feasibility of ML-based approaches as a tool to support faster detection of recurring malignancies, while we also highlight the challenges and pitfalls that have to be solved in order to reliably determine MRD based on MFC data. Keywords: Measurable residual disease, Artificial intelligence, Quantitation by flow cytometry, Acute myeloid leukemia