Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy.

Abstract

The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in JAVA, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a "gold standard" database which exhibit spectral and spatial profiles which are most similar to a given query image. The system suggests the most likely diagnosis based on majority logic of the retrieved cases. The system was used to discriminate among three lymphoproliferative disorders and healthy cells. The system provided the correct classification in more than 83% of the cases studied. System performance was evaluated using rigorous statistical assessment and by comparison with human observers.