Anaplastic large cell lymphoma (ALCL) is a distinct type of T/null-cell non-Hodgkin lymphoma that commonly involves nodal and extranodal sites. The World Health Organization of lymphoid neoplasms recognizes two types: anaplastic lymphoma kinase (ALK) positive or ALK negative, the former as a result of abnormalities involving the ALK gene at chromosome 2p23. Patients with ALCL rarely develop a leukemic phase of disease, either at the time of initial presentation or during the clinical course. Described herein is a patient with ALK+ ALCL, small cell variant, associated with the t(2;5)(p23;q35), who initially presented with leukemic involvement and an extraordinarily high leukocyte count of 529 x 10(9)/L, which subsequently peaked at 587 x 10(9)/L. Despite chemotherapy the patient died 2(1/2) months after diagnosis. In the literature review 20 well-documented cases are identified of ALCL in leukemic phase reported previously, with a WBC ranging from 15 to 151 x 10(9)/L. Leukemic phase of ALCL occurs almost exclusively in patients with ALK+ ALCL, most often associated with the small cell variant and the t(2;5)(p23;q35), similar to the present case. Patients with leukemic phase ALK+ ALCL appear to have a poorer prognosis than most patients with ALK+ ALCL.

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.