Ovarian dysplasia in epithelial inclusion cysts. A morphometric approach using neural networks

Abstract

Background: Ovarian dysplasia has been described in the ovarian surface epithelium by histologic and morphometric studies. This study evaluates ovarian dysplasia in epithelial inclusion cysts adjacent to overt carcinoma and also incidentally found in ovaries removed for nonneoplastic diseases, including oophorectomies for family history of ovarian cancer, using an artificial neural network.

Methods: Histologic sections from 37 ovaries of which 26 were diagnosed with dysplasia in epithelial inclusion cysts (10 adjacent to carcinoma and 16 incidental) and 11 with benign epithelial inclusion cysts were evaluated by tracing nuclear profiles and assessing measures of nuclear area, shape, and texture. These sections were analyzed using artificial neural networks and also statistically using the Kruskal-Wallis test with the Dunn procedure to compare the morphologic similarity of dysplasia found incidentally in inclusion cysts unrelated to carcinoma from that in inclusion cysts adjacent to carcinoma.

Results: Neither statistical nor artificial neural network analysis was able to distinguish between incidental and adjacent dysplasia. Both types differed significantly from the control cases.

Conclusions: Neural networks are powerful classification tools when applied to multiple variables extracted from individual cases. In this study, they helped to substantiate the similarity between dysplasia found incidentally and that adjacent to ovarian carcinoma. Because dysplasia represents a potential precancerous lesion, its incidental finding may help identify patients at risk for developing ovarian carcinoma.