Prediction of cervical neoplasia diagnosis groups. Discriminant analysis on digitized cell images.
The purpose of this study was to develop discriminant analysis models for predicting cervical dysplasia/neoplasia case diagnoses using cytometric features derived from the digital image analysis of cell monolayers. The data base consisted of 925 cells from 27 cases diagnosed either as moderate dysplasia (n = 10), severe dysplasia (n = 5), carcinoma in situ (n = 8) or invasive carcinoma (n = 4) on both tissue biopsy and monolayer preparations. Cell features examined were cell diameter, nuclear diameter, nuclear mean optical density (OD), nuclear integrated OD (IOD), nuclear OD standard deviation, normalized IOD, nuclear texture and nuclear-cytoplasmic ratio. Features derived from cells visually classified as moderate dysplasia correctly predicted the case diagnosis of moderate dysplasia versus more severe disease for 85% of the cells. Prediction models using summary measures (mean and variance) derived from all visually classified abnormal cells within each case correctly separated all cases into their respective diagnostic categories. These findings suggest that dysplastic cells in a cytologic sample have features that collectively reflect the tissue diagnosis, regardless of the visual differences among the cells. Such information has potential use for diagnosis and possibly for prognosis.
D002278:Carcinoma in Situ / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology; D003593:Cytoplasm / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D011379:Prognosis; D013223:Statistics as Topic; D002578:Uterine Cervical Dysplasia / Q000473:pathology*; D002583:Uterine Cervical Neoplasms / Q000473:pathology*; D014626:Vaginal Smears