Journal
CURRENT OPINION IN ONCOLOGY
Volume 32, Issue 2, Pages 162-169Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/CCO.0000000000000607
Keywords
computational; diagnosis; flow cytometry; hematology; minimal residual disease; relapse
Categories
Funding
- European Union [634789]
- MDS-RIGHT
Ask authors/readers for more resources
Purpose of review This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies. Recent findings 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available