Objective: To investigate the potential of artificial neural networks for cell identification in endometrial lesions from postmenopausal women.
Study design: The study was performed on cytologic material obtained by the Gynoscann endometrial cell samplerfrom 12 cases of atrophic endometrium, 48 cases of hyperplasia without cytologic atypia (18 cases of simple hyperplasia and 30 cases of complex hyperplasia), 12 cases of hyperplasia with cytologic atypia (complex atypical hyperplasia) and 48 cases of adenocarcinoma (30 cases of well-differentiated, 12 cases of moderately differentiated and 6 cases of poorly differentiated carcinoma). From each case approximately 100 cells were examined using a custom image analysis system. A learning vector quantizer (LVQ) identified the collected data.
Results: Investigation of cells from Endometrial Alterations with LVQ proved that according to the nuclear characteristics, as expressed by morphometric and textural measures, the endometrial cells from postmenopausal women may be identified as belonging to one of thefollowing three groups: atrophy, hyperplasia without cytologic atypia (simple and complex hyperplasia) and malignant neoplastic lesions (atypical complex and adenocarcinoma).
Conclusion: The role of nuclear morphologic features in the cytologic diagnosis of endometrial alterations was confirmed. The overlap in thefeature space observed indicates that cell characteristics do not form strictly separate clusters. Thatfact explains the difficulty that morphologists have with the reproducible identification of cells from endometrial lesions in postmenopausal women. Application of LVQ offers a good classification at the cell level and promises to be a powerful toolfor classification on the individual patient level andfor the clarification of the natural history of endometrial pathology.