title: Optimizing antibody panels for efficient and cost-effective flow cytometric diagnosis of acute leukemia.
abstract: BACKGROUND
  Differentiating acute myeloid leukemia (AML) from acute lymphoblastic leukemia (ALL) determines effective patient management and often depends on flow cytometry. Antibodies used in flow cytometry are costly, and the expenses are not always reimbursed. Having observed that AML and ALL have distinct patterns in the CD45/SSC panel, we set to analyze more leukemia cases and establish an algorithm for the efficient diagnosis of acute leukemia.
  
  
  METHODS
  We retrospectively analyzed 127 consecutive cases of acute leukemia within the last 2 years and correlated the blast distribution patterns in the CD45/SSC panel, with the morphology and the detailed immunophenotype.
  
  
  RESULTS
  Our results show that all the acute leukemias can be initially triaged into AML, ALL, and Indeterminate provisional groups based on the blast distribution patterns in the CD45/SSC panel and morphology. Each group was then further analyzed with tailored AML, ALL, and Indeterminate flow panels. Using this approach, we have efficiently and correctly diagnosed almost all the acute leukemias. Our analysis also determined the minimal numbers of immunological markers needed for the lineage assignment of acute leukemia.
  
  
  CONCLUSIONS
  The algorithmic approach with tailored subsequent antibody selection could maintain diagnostic accuracy while significantly reducing reagent use, labor, and time. With a shrinking reimbursement for flow cytometric studies, an increase in laboratory efficiency without compromising diagnostic accuracy or turnaround time will contribute to preserving revenue and optimizing clinical service.
authors.affiliation: Department of Pathology, University of Maryland Medical Center, Baltimore, Maryland 21201, USA.
authors.forename: Neil G
authors.initials: NG
authors.lastname: Haycocks
authors.forename: Lenore
authors.initials: L
authors.lastname: Lawrence
authors.forename: Joseph W
authors.initials: JW
authors.lastname: Cain
authors.forename: X Frank
authors.initials: XF
authors.lastname: Zhao
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000906:Antibodies; D000954:Antigens, Surface / Q000378:metabolism; D015415:Biomarkers / Q000378:metabolism; D048430:Cell Shape; D002648:Child; D002675:Child, Preschool; D003937:Diagnosis, Differential; D005260:Female; D005434:Flow Cytometry / Q000191:economics / Q000379:methods*; D006801:Humans; D007223:Infant; D015448:Leukemia, B-Cell / Q000175:diagnosis* / Q000276:immunology; D015470:Leukemia, Myeloid, Acute / Q000175:diagnosis* / Q000276:immunology; D015458:Leukemia, T-Cell / Q000175:diagnosis* / Q000276:immunology; D017493:Leukocyte Common Antigens / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D012189:Retrospective Studies; D055815:Young Adult
chemical_list: D000906:Antibodies; D000954:Antigens, Surface; D015415:Biomarkers; D017493:Leukocyte Common Antigens