We propose a clinically oriented classification scheme consisting of diagnostic categories with well determined criteria. Each diagnostic category is linked with an implied risk of malignancy; thus, clinicians may decide on patient management and eventually reduce unnecessary interventional diagnostic procedures. Diagn. Cytopathol. 2016;44:888-901. © 2016 Wiley Periodicals, Inc.

Computerized systems based on ANNs can aid the cytological classification of endometrial nuclei and lesions with sufficient sensitivity and specificity. Diagn. Cytopathol. 2017;45:202-211. © 2016 Wiley Periodicals, Inc.

Background: miRNAs have an important role as their deregulation is linked to endometrial cancer.Methods: A custom miScript® miRNA PCR Array was used to investigate for the first time the expression of eight miRNAs in forty-nine histologically confirmed Liquid Based cytology endometrial samples. The expression profile of the same miRNAs was also examined in sixty formalin-fixed tissue samples.Results: Expression of seven miRNAs was significantly higher in malignant samples with three of them (mir-182, mir-141 and mir-205) performing optimally.Conclusion: These results suggest the potential use of this non-invasive method of sampling for miRNA expression studies. Furthermore miRNA overexpression could serve as an ancillary or reflex test for optimal identification of malignant samples especially in morphologically inadequate samples.

Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.

Endometrial cancer is the most common malignancy of the female genital tract while aberrant DNA methylation seems to play a critical role in endometrial carcinogenesis. Galanin's expression has been involved in many cancers. We developed a new pyrosequencing assay that quantifies DNA methylation of galanin's receptor-1 (GALR1). In this study, the preliminary results indicate that pyrosequencing methylation analysis of GALR1 promoter can be a useful ancillary marker to cytology as the histological status can successfully predict. This marker has the potential to lead towards better management of women with endometrial lesions and eventually reduce unnecessary interventions. In addition it can provide early warning for women with negative cytological result.