Using classification and regression trees, liquid-based cytology and nuclear morphometry for the discrimination of endometrial lesions.

'The objective of this study is to investigate the potential of classification and regression trees (CARTs) in discriminating benign from malignant endometrial nuclei and lesions. The study was performed on 222 histologically confirmed liquid based cytological smears, specifically: 117 benign cases, 62 malignant cases and 43 hyperplasias with or without atypia. About 100 nuclei were measured from each case using an image analysis system; in total, we collected 22783 nuclei. The nuclei from 50% of the cases (the training set) were used to construct a CART model that was used for knowledge extraction. The nuclei from the remaining 50% of cases (test set) were used to evaluate the stability and performance of the CART on unknown data. Based on the results of the CART for nuclei classification, we propose two classification methods to discriminate benign from malignant cases. The CART model had an overall accuracy for the classification of endometrial nuclei equal to 85%, specificity 90.68%, and sensitivity 72.05%. Both methods for case classification had similar performance: overall accuracy in the range 94-95%, specificity 95%, and sensitivity 91-94%. The results of the proposed system outperform the standard cytological diagnosis of endometrial lesions. This study highlights interesting diagnostic features of endometrial nuclear morphology and provides a new classification approach for endometrial nuclei and cases. The proposed method can be a useful tool for the everyday practice of the cytological laboratory.