Affiliations: Department of Pathology, University Hospital Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands | Cancer Imaging Department, Medical Physics Division, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 1L3
Abstract: A retrospective analysis was performed on archival cervical smears from a group of 56 women with cervical intraepithelial neoplasia (CIN), who had received follow‐up by cytology only. Automated image cytometry of Feulgen‐stained DNA was used to determine the differences between progressive and regressive lesions. The first group of 30 smears was from women who had developed cancer after initial smears with dysplastic changes (progressive group). The second group of 26 smears with dysplastic changes had shown regression to normal (regressive group). The goal of the study was to determine if differences in cytometric features existed between the progressive and regressive groups. CIN categories I, II and III were represented in both groups, and measurements were pooled across diagnostic categories. Images of up to 700 intermediate cells were obtained from each slide, and cells were scanned exhaustively for the detection of diagnostic cells. Discriminant function analysis was performed for both intermediate and diagnostic cells. The most significant differences between the groups were found for diagnostic cells, with a cell classification accuracy of 82%. Intermediate cells could be classified with 60% accuracy. Cytometric features which afforded the best discrimination were characteristic of the chromatin organization in diagnostic cells (nuclear texture). Slide classification was performed by thresholding the number of cells which exhibited progression associated changes (PAC) in chromatin configuration, with an accuracy of 93 and 73% for diagnostic and intermediate cells, respectively. These results indicate that regardless of the extent of nuclear atypia as reflected in the CIN category, features of chromatin organization can potentially be used to predict the malignant or progressive potential of CIN lesions.
Keywords: Uterine cervix, dysplasia, image analysis, automation, DNA
