A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms

Journal of Pathology Informatics
Cesar Angeletti

Abstract

Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described. Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system. Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912. The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

References

Oct 4, 2005·Laboratory Investigation; a Journal of Technical Methods and Pathology·Cesar AngelettiDavid L Rimm
Feb 12, 2013·Nature Methods·Nima AghaeepourRichard H Scheuermann

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Citations

Apr 30, 2021·Seminars in Cancer Biology·Jamal Elkhader, Olivier Elemento

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Methods Mentioned

BETA
flow cytometry
feature extraction
FCS
Flow

Software Mentioned

MEAN
Flexible Transport System
Flexible Image Transport System ( FITS )
Flexible
GENIE
tiff
ALADDIN
LINUX
FlowCAP
Prada

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