Biological behavior of CIN lesions is predictable by multiple parameter logistic regression models.

Abstract

OBJECTIVES: Progression and regression of premalignant cervical lesions cannot be predicted using conventional cytomorphological or histomorphological parameters. However, markers such as human papillomavirus (HPV) or makers indicating proliferation, genetic instability and chromosomal aberration may be of predictive value assessing short-term biological behavior of cervical intraepithelial neoplasia. In this paper, we have studied the usage of logistic regression models with Ki-67 labeling index (LI), chromosome index for chromosome 1 (CI#1) and aneusomy for chromosome 1 in cervical smears to predict progressive and regressive behavior of premalignant cervical lesions. METHODS: Retrospectively, the intake smears of 42 women showing regression in follow-up and of 31 women showing progression in follow-up were assessed. RESULTS: A multiparameter logistic regression model containing the parameters Ki-67 LI, CI#1 and the fraction of cells with four copies of chromosome 1 per nucleus appeared to be the best predicting model, overall correct classification of 93.2% (area under the receiver operating characteristic curve 0.96 +/- 0.02). After cross-validation, the model correctly classified 66 of 73 samples (90.4%). Moreover, the model predicted biological behavior perfectly assessing the smear taken subsequently to the intake smear of 46 women. CONCLUSION: Although measuring parameters indicating proliferation and chromosome 1 aberration is laborious, this study demonstrates that short-term progressive and regressive behavior is highly predictable using a model combing these parameters. We also showed that in the triage management of high-risk human papillomavirus-positive women with minimally abnormal smears applying a model as such can be useful.