子宫内膜异位症是育龄妇女常见的良性疾病，恶变率约为1%。子宫内膜异位症相关卵巢癌（EAOC）通常发生在卵巢，严重威胁女性健康。早期识别EMs恶变高危人群对于EAOC的防治具有重要意义。但目前仍缺乏特异、敏感的预测因素。近年来，国内外学者利用传统统计方法和机器学习探索EAOC相关预测因素和预测模型。本文主要对EAOC的诊断和预测模型进行综述和评价。通过检索CNKI、PubMed和Web of Science核心合集（WOSCC）截至2023年的研究进行鉴定，对符合临床研究纳入标准的数据进行质量评价。本文对文献中的预测因素和预测模型进行了分析和总结。经过筛选，最终获得了7篇相关研究。预测因素包括：年龄、月经、绝经状态、病程、子宫内膜异位症相关不孕、绝经期间单一雌激素使用史、血清学指标：人附睾蛋白4、碳水化合物抗原125（CA125）、卵巢恶性肿瘤风险算法、适应症超声检查：囊肿形态、结构、血流信号等。预测模型：比对图、多元logistic回归模型、Gail模型、Gradient Boosting Decision Tree、Lasso-logistics回归。相关模型与实际情况吻合较好，具有良好的敏感性和特异性。总结了相关预测因素和预测模型，为EAOC领域的预测模型研究提供参考和新思路，以期制定规范的EAOC高危人群长期管理策略，实现EAOC的推进。 EAOC 患者的诊断阈值。版权所有 © 2024 作者。由 Wolters Kluwer Health, Inc. 出版

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.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.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.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.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.