ArticleFrontiers in medicine2025
A risk-stratified model for predicting endometrial atypical hyperplasia and cancer to guide biopsy decisions in asymptomatic postmenopausal women.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A machine learning-based risk prediction framework for atypical hyperplasia and endometrial cancer in postmenopausal women.World journal of surgical oncology · 2026Article
- Endometrial thickness evaluation with ultrasonography assessment among normal and endometrial cancer across multi-ethnic Indonesian women.Frontiers in reproductive health · 2026Article
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Authors and funding
5 authors.
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Abstract
Background: Endometrial atypical hyperplasia (EAH) and endometrial cancer (EC) are increasingly detected in asymptomatic postmenopausal women. This often leads to delayed treatment. Risk stratification remains challenging, and single-factor models may not accurately identify high-risk individuals. This study aimed to develop and validate a multivariable prediction model for identifying EAH or EC in asymptomatic postmenopausal women. Methods: This retrospective cohort study included asymptomatic postmenopausal women with endometrial pathology records from the Third Affiliated Hospital of Sun Yat-sen University, China (2021-2024). Candidate risk factors included demographics, clinical characteristics, and hematological parameters. The primary outcome was a composite of histologically-confirmed EAH or EC. Multivariable Poisson regression with robust variance was then employed to identify independent risk factors for this composite outcome. Risk - stratified models were developed by calculating predicted probabilities for key combinations of risk factors. Results: Among 928 patients [median age: 59 years, IQR (interquartile range): 55-65; median BMI: 23.4 kg/m Discussion: The risk of EAH and EC among asymptomatic postmenopausal women varies significantly based on clinical factors. This risk-stratified modeling approach delivers individualized risk estimates to inform endometrial biopsy decisions, facilitating personalized patient management.
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