Navigating the future of fertility preservation: advanced predictive strategies for treatment outcomes of endometrial atypical hyperplasia and carcinoma
- Author:
Tianwei XING
1
;
Huiyang LI
;
Ping-Li SUN
;
Hongwen GAO
Author Information
- Publication Type:Review Article
- From:Journal of Gynecologic Oncology 2025;36(6):e123-
- CountryRepublic of Korea
- Language:English
- Abstract: Due to the decreasing age of onset and the postponement of childbearing, there is a growing number of patients with endometrial carcinoma (EC) and endometrial atypical hyperplasia (EAH) seeking fertility-sparing treatments. Progestogen-based therapy serves as the principal conservative approach for EC. However, the variability in treatment outcomes hampers the potential for delivering more tailored therapies in clinical practice. To better guide the treatment of patients with fertility preservation needs, we conducted a comprehensive review of existing literature to explore factors related to molecular classification, biomarkers and artificial intelligence (AI) technology that may predict fertility-sparing treatment outcomes, we also looked ahead to future research directions in this field. The pathology before and after treatment is the primary basis for assessing the effectiveness of fertilitysparing treatment for EC and EAH. However, it is challenging to predict the therapeutic outcomes based on the pathological morphology of the initial diagnosis. Traditional immunohistochemical markers, such as estrogen and progesterone receptors, are also very limited in predicting therapeutic response. In recent years, the prognosis of fertilitysparing treatment has also been considered to be correlated with the molecular classification and gene mutation markers of EC. However, there are currently few direct clinical studies available, and our focus will be on reviewing these studies and assessing their applicability.In addition, there are some studies utilizing AI to predict the molecular classification, genes and therapeutic response of EC. The integration of these features will aid in the development of advanced predictive strategies for fertility-sparing treatment of EC and EAH.
