title: A teaching database for diagnosis of hematologic neoplasms using immunophenotyping by flow cytometry.
abstract: BACKGROUND
  In the diagnosis of lymphomas and leukemias, flow cytometry has been considered an essential addition to morphology and immunohistochemistry. The interpretation of immunophenotyping results by flow cytometry involves pattern recognition of different hematologic neoplasms that may have similar immunologic marker profiles. An important factor that creates difficulty in the interpretation process is the lack of consistency in marker expression for a particular neoplasm. For this reason, a definitive diagnostic pattern is usually not available for each specific neoplasm. Consequently, there is a need for decision support tools to assist pathology trainees in learning flow cytometric diagnosis of leukemia and lymphoma.
  
  
  OBJECTIVE
  Development of a Web-enabled relational database integrated with decision-making tools for teaching flow cytometric diagnosis of hematologic neoplasms.
  
  
  METHODS
  This database has a knowledge base containing patterns of 44 markers for 37 hematologic neoplasms. We have obtained immunophenotyping data published in the scientific literature and incorporated them into a mathematical algorithm that is integrated to the database for differential diagnostic purposes. The algorithm takes into account the incidence of positive and negative expression of each marker for each disorder.
  
  
  RESULTS
  Validation of this algorithm was performed using 92 clinical cases accumulated from 2 different medical centers. The database also incorporates the latest World Health Organization classification for hematologic neoplasms.
  
  
  CONCLUSIONS
  The algorithm developed in this database shows significant improvement in diagnostic accuracy over our previous database prototype. This Web-based database is proposed to be a useful public resource for teaching pathology trainees flow cytometric diagnosis.
authors.affiliation: Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA. Nghia.D.Nguyen@uth.tmc.edu
authors.forename: Andy N D
authors.initials: AN
authors.lastname: Nguyen
authors.forename: Jitakshi
authors.initials: J
authors.lastname: De
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Nguyen
authors.forename: Anthony
authors.initials: A
authors.lastname: Padula
authors.forename: Zhenhong
authors.initials: Z
authors.lastname: Qu
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004503:Education, Medical, Graduate; D005434:Flow Cytometry / Q000379:methods*; D019337:Hematologic Neoplasms / Q000175:diagnosis* / Q000276:immunology / Q000378:metabolism; D006801:Humans; D016130:Immunophenotyping; D010338:Pathology, Clinical / Q000193:education*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor