Risk-stratification Machine Learning Model Using Demographic Factors, Gynaecological Symptoms and Β-Catenin for Endometrial Hyperplasia and Carcinoma: a Cross-Sectional Study.

Demographic features, suggestive gynaecological symptoms, and immunohistochemical expression of endometrial β-catenin have a prognostic capacity for endometrial hyperplasia and carcinoma. This study assessed the interaction of all variables and developed risk stratification for endometrial hyperplasia and carcinoma. This cross-sectional study was conducted from January 2023 to July 2023 at two teaching hospitals in Makassar Indonesia. Patients (< 70 years old) with suggestive symptoms of endometrial hyperplasia or carcinoma or being referred with disease code N.85 who underwent curettage and/or surgery for pathology assessment except those receiving radiotherapy, or chemotherapy, presence of another carcinoma, coagulation disorder, and history of anti-inflammatory drug use and unreadable samples. Demographic, and clinical symptoms were collected from medical records. Immunohistochemistry staining using mouse-monoclonal antibodies determined the β-catenin expression (percentage, intensity, and H-score) in endometrial tissues. Ordinal and Binary Logistic regression identified the potential predictors to be included in neural networks and decision tree models of histopathological grading according to the World Health Organization/WHO grading classification. Abdominal enlargement was associated with worse pathological grading (adjusted odds ratio/aOR 6.7 95 Risk stratification based on demographics, clinical symptoms, and β-catenin possesses a good performance in differentiating non-atypical hyperplasia with later stages.