PROGNOSIS FOR ENDOMETRIAL HYPERPLASIA PROGRESSION IN PREMENOPAUSAL AND MENOPAUSAL WOMEN BASED ON THE ANALYSIS OF CELLULAR IMMUNITY INDICATORS USING MULTIPARAMETRIC NEURAL NETWORK CLUSTERING - Selskyy, Petro; Hladii, Olena; Heryak, Svitlana; Sverstiuk, Andrii; Slyva, Andrii; Televiak, Anatolii; Parahnyuk, Iryna; Golovata, Tetyana; Orel, Yurii; Adam, Tetiana (Eastern Ukrainian Medical Journal, 2025)
Many factors play a role in the progression of endometrial hyperplasia and the increased risk of malignant transformation. One of the important factors influencing pathological tissue remodeling is the immune response. However, changes in cellular immunity have not yet been systematized into specific patterns of immunological response in hyperplasia. Therefore, the implementation of easy-to-use and relatively inexpensive information technologies and risk factor analysis techniques is particularly important.The objective of the study was to develop methods for predicting endometrial hyperplasia progression based on the analysis of morphological markers and indicators of cellular immunity using multiparametric neural network clustering.Materials and Methods. The indicators of the cellular component of general immunity were determined in 43 pre- and menopausal women, of whom 31 patients were diagnosed with endometrial hyperplasia without atypia, and 12 women were otherwise healthy and formed the control group. For deeper analysis, we applied an approach based on multiparameter neural network clustering using NeuroXL Classifier for Microsoft Excel.Results. In patients with endometrial hyperplasia, suppression of cellular immunity with a significant decrease in the percentage of all lymphocyte subpopulations was detected, whereas no significant changes in the immunoregulatory index were observed. It can indicate sufficient compensatory capabilities of the immune defense. The results of cluster analysis showed that in order to predict the progression of endometrial hyperplasia based on the analysis of the cellular immunity, it is important to consider the combination of reduced levels of CD3+ T-lymphocytes, CD4+ T-lymphocytes, and CD8+ T-lymphocytes, and increased levels of CD3+CD56+ NKT-like cells and CD56+ NK cells.Conclusions. Neural network clustering was used to objectively classify patients into risk groups for progression of endometrial hyperplasia based on the results of clustering the studied indicators, which allows determining the significance of combined changes in certain parameters for disease progression prognosis.