CONTEXT: 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. DESIGN: 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.
CONTEXT: 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. DESIGN: 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: Christian Castaneda; Kip Nalley; Ciaran Mannion; Pritish Bhattacharyya; Patrick Blake; Andrew Pecora; Andre Goy; K Stephen Suh Journal: J Clin Bioinforma Date: 2015-03-26