Computer-assisted differential diagnosis of malignant mesothelioma based on syntactic structure analysis

Background: Malignant mesothelioma, a mesoderm-derived tumor, is related to asbestos exposure and remains a diagnostic challenge because none of the g enetic or immunohistochemical markers have yet been proven to be specific. To assist in the identification of mesothelioma and to differentiate it fro m other common lesions at the same location, we have tested the performance of syntactic structure analysis (SSA) in an auto mated classification proc edure. Materials and Methods: Light-microscopic images of tissue sections of malig nant mesothelioma, hyperplastic mesothelium, and adenocarcinoma were analyz ed using parameters selected from the Voronoi diagram, Gabriel's graph, and the minimum spanning tree which were classified with a K-nearest-neighbor algorithm. Results: Results showed that mesotheliomas were diagnosed correctly in 74% of the cases; 76% of the adenocarcinomas were correctly graded, and 88% of the mesotheliomas were correctly typed. The performance of the parameters w as dependent on the obtained classification (i.e., tumor-tumor versus tumor -benign). Conclusions: Our results suggest that SSA is valuable in the differential c lassification of mesothelioma and that it supplements a visually appraised diagnosis. The recognition scores may be increased by a combination of SSA with, for example, cellular or nuclear parameters, measured at higher magni fications to form a solid base for fully automated expert systems. Cytometr y 35:23-29, 1999. (C) 1999 Wiley-Liss,Inc.