Abstract
BACKGROUND: Endometriosis is a common benign disease in women of childbearing age, with a malignant change rate of about 1%. Endometriosis associated ovarian cancer (EAOC), which usually occurs in the ovaries, is a serious threat to women's health. Early identification of high-risk groups of EMs malignant transformation is of great significance for the prevention and treatment of EAOC. However, there is still a lack of specific and sensitive prediction factors. In recent years, scholars at home and abroad have used traditional statistical methods and machine learning to explore EAOC related prediction factors and prediction models. This paper mainly reviews and evaluates the diagnosis and prediction model of EAOC. METHODS: Studies were identified by searching the CNKI, PubMed and Web of Science Core Collection, (WOSCC) till 2023, Data which met the inclusion criteria of clinical studies were evaluated about the quality. This paper analyzes and summarizes the prediction factors and prediction models in the literature. RESULTS: After screening, 7 relevant studies were finally obtained. Prediction factors included: age, menstruation, menopausal status, course of disease, infertility associated with endometriosis, history of single estrogen use during menopause, serological indexes: human epididymis protein 4, carbohydrate antigen 125(CA125), ovarian malignancy risk algorithm, indications for ultrasound examination: cyst shape, structure and blood flow signal, etc. Prediction models: Alignment diagram, Multivariate logistic regression model, Gail model, Gradient Boosting Decision Tree and Lasso-logistics regression. CONCLUSION: Related models were in good agreement with the actual situation, and have good sensitivity and specificity. The relevant prediction factors and prediction models were summarized to provide reference and new thinking for the research of prediction models in the field of EAOC, in order to develop standardized long-term management strategies for high-risk groups of EAOC and realize the advance of the diagnosis threshold of patients with EAOC.