Evaluation Analysis of miRNAs Overexpression in Liquid-Based Cytology Endometrial Samples.

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.

Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions.

BACKGROUND: This study aims to investigate the efficacy of an Artificial Neural Network based on Multi-Layer Perceptron (ANN-MPL) to discriminate between benign and malignant endometrial nuclei and lesions in cytological specimens. METHODS: We collected 416 histologically confirmed liquid-based cytological smears from 168 healthy patients, 152 patients with malignancy, 52 with hyperplasia without atypia, 20 with hyperplasia with atypia, and 24 patients with endometrial polyps. The morphometric characteristics of 90 nuclei per case were analyzed using a custom image analysis system; half of them were used to train the MPL-ANN model, which classified each nucleus as benign or malignant. Data from the remaining 50% of cases were used to evaluate the performance and stability of the ANN. The MLP-ANN for the nuclei classification (numeric and percentage classifiers) and the algorithms for the determination of the optimum threshold values were estimated with in-house developed software for the MATLAB v2011b programming environment; the diagnostic accuracy measures were also calculated. RESULTS: The accuracy of the MPL-ANN model for the classification of endometrial nuclei was 81.33%, while specificity was 88.84% and sensitivity 69.38%. For the case classification based on numeric classifier the overall accuracy was 90.87%, the specificity 93.03% and the sensitivity 87.79%; the indices for the percentage classifier were 95.91%, 93.44%, and 99.42%, respectively. CONCLUSION: 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.

A reporting system for endometrial cytology: Cytomorphologic criteria-Implied risk of malignancy.

BACKGROUND: There have been various attempts to assess endometrial lesions on cytological material obtained via direct endometrial sampling. The majority of efforts focus on the description of cytological criteria that lead to classification systems resembling histological reporting formats. These systems have low reproducibility, especially in cases of atypical hyperplasia and well differentiated carcinomas. Moreover, they are not linked to the implied risk of malignancy. METHODS: The material was collected from women examined at the outpatient department of four participating hospitals. We analyzed 866 consecutive, histologically confirmed cases. The sample collection was performed using the EndoGyn device, and processed via Liquid Based Cytology, namely ThinPrep technique. The diagnostic categories and criteria were established by two cytopathologists experienced in endometrial cytology; performance of the proposed reporting format was assessed on the basis of histological outcome; moreover, the implied risk of malignancy was calculated. RESULTS: The proposed six diagnostic categories are as follows: (i) nondiagnostic or unsatisfactory; (ii) without evidence of hyperplasia or malignancy; (iii) atypical cells of endometrium of undetermined significance; (iv) atypical cells of endometrium of low probability for malignancy; (v) atypical cells of endometrium of high probability for malignancy; and (vi) malignant. The risk of malignancy was 1.42% ± 0.98%, 44.44% ± 32.46% (nine cases), 4.30% ± 4.12%, 89.80% ± 8.47%, and 97.81% ± 2.45%, respectively. CONCLUSION: 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.

A Pyrosequencing Assay for the Quantitative Methylation Analysis of GALR1 in Endometrial Samples: Preliminary Results.

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.

Classification of endometrial lesions by nuclear morphometry features extracted from liquid-based cytology samples: a system based on logistic regression model.

OBJECTIVE: To investigate the potential of a computerized system for the discrimination of benign from malignant endometrial nuclei and lesions. STUDY DESIGN: A total of 228 histologically confirmed liquid-based cytological smears were collected: 117 within normal limits cases, 66 malignant cases, 37 hyperplasias without atypia, and 8 cases of hyperplasia with atypia. From each case we extracted nuclear morphometric features from about 100 nuclei using a custom image analysis system. Initially we performed feature selection, and subsequently we applied a logistic regression model that classified each nucleus as benign or malignant. Based on the results of the nucleus classification process, we constructed an algorithm to discriminate endometrium cases as benign or malignant. RESULTS: The proposed system had an overall accuracy for the classification of endometrial nuclei equal to 83.02%, specificity of 85.09%, and sensitivity of 77.01%. For the case classification the overall accuracy was 92.98%, specificity was 92.86%, and sensitivity was 93.24%. CONCLUSION: The proposed computerized system can be applied for the classification of endometrial nuclei and lesions as it outperformed the standard cytological diagnosis. This study highlights interesting diagnostic features of endometrial nuclear morphology, and the proposed method can be a useful tool in the everyday practice of the cytological laboratory.