Presented at the 1998 APIII Conference Return to 1998 Abstract Index
IMAGE-GUIDED DECISION SUPPORT FOR DETECTING AND DIFFERENTIATING LYMPHOPROLIFERATIVE DISORDERS FROM PERIPHERAL BLOOD SMEARS
University of Medicine and Dentistry of New Jersey (UMDNJ)
Piscataway, New Jersey
Lauri A. Goodell, Dorin Comaniciu, Peter Meer, David J.
Foran
For additional information, see URL: http://pleiad.umdnj.edu/igds/
The subtle visible differences exhibited by some malignant
lymphomas and chronic lymphocytic leukemia give rise to
a significant number of false negatives during routine screening
by medical technologists. Mantle cell lymphoma is a recently
described entity which is often misdiagnosed as lymphocytic
leukemia (CLL)/small lymphocytic lymphoma(SLL), or small
cleaved cell lymphoma (SCCL). A prototypical image guided
decision support (IGDS) system has been developed with the
goal of aiding the medical technologist, pathology resident
or pathologist in the detection and differentiation of abnormal
lymphoid cell populations.
While traditional database systems utilize text-based information to search through databases, the image-guided decision support (IGDS) approach systematically searches through databases of consensus-graded medical cases based upon the visual content of constituent pathology image records. During preliminary, feasibility studies, the image-guided decision support prototype automatically delineated salient biological structures from digitized microscopic specimens and characterized the underlying pathology using a set of non-traditional spatial and spectral signatures. A weighted mixture of these measurements served as search criteria in a series of systematic queries which retrieved diagnoses, correlated clinical data, and image records of consensus-graded cases which exhibited spatial and spectral profiles which were consistent with those of the undiagnosed case. Utilizing a unique mulitmodal fusion agent, the system is voice activated and provides audio and graphical feedback. The prototype provided the correct classification, based on majority logic among retrieved cases, in approximately 86% of the trials. Studies comparing these classification rates with those of medical technologists and pathology residents are ongoing to determine the systems value as an educational tool.
