This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies.
In recent years, computational analysis methods ...have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary.
Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies.
Summary
Most patients with myelodysplastic syndromes (MDS) require therapeutic intervention. However, there are few approved treatments for MDS. To explore reasons, we searched clinicaltrials.gov and ...clinicaltrialsregister.eu for MDS trials from 2000 to 2020. We assessed which agents were under investigation and analysed clinical trial characteristics and continuation rates from phase I to II to III to approval. As such, we identified 384 unique agents in 426 phase I, 430 phase II and 48 phase III trials. Success rates for phase III trials and agents were low, and MDS trials took markedly longer to complete than the average clinical trial. Although success rates were higher when MDS‐specific phase I trials were conducted, 52% of the agents had not been evaluated in a phase I trial for MDS. MDS trials often failed to include quality of life, an especially important outcome for older MDS patients. Our work identifies factors potentially contributing to the paucity of available agents for MDS. We suggest a framework to improve clinical research in MDS that might ultimately augment the number of available agents.
The diagnostic work‐up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively ...validated a fully computational tool for flow cytometry diagnostics in suspected‐MDS. The computational diagnostic workflow consists of methods for pre‐processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).
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