Evaluating the Effectiveness and Challenges of Endometrial Cancer Risk Prediction Models for Clinical Use: A Systematic Review
By
Sabine El-Halabi
Alison Zhijin Luo
Aline Talhouk
November 19, 2025
Clinical Scorecard: Evaluating the Effectiveness and Challenges of Endometrial Cancer Risk Prediction Models for Clinical Use: A Systematic Review
At a Glance
Category Detail
Condition Endometrial Cancer (EC)
Key Mechanisms Rising obesity levels, genetic predisposition (e.g., Lynch syndrome), and modifiable risk factors.
Target Population Asymptomatic women, particularly those at higher risk due to obesity and genetic factors.
Care Setting Clinical settings focusing on cancer prevention and early detection.
Key Highlights
Endometrial cancer is the most common gynecological malignancy in high-income countries. Five-year survival rate exceeds 95% if detected early; drops to 18% if metastasized. No universal screening recommendations exist except for individuals with Lynch syndrome. Multivariable predictive models can estimate individual risk based on various factors. Effective prevention strategies include weight loss, physical activity, and hormonal interventions.
Guideline-Based Recommendations
Diagnosis
Regular transvaginal ultrasounds and endometrial biopsies for individuals with Lynch syndrome.
Management
Targeted interventions for high-risk individuals to reduce EC risk.
Monitoring & Follow-up
Continuous evaluation of risk prediction models for accuracy and applicability.
Risks
Increased mortality rates among Black women and under-represented ethnicities due to aggressive disease forms.
Patient & Prescribing Data
Women without symptoms or suspicion of cancer, particularly those with obesity and genetic predispositions.
Modifiable risk factors account for about 40% of EC cases; interventions can significantly reduce risk.
Clinical Best Practices
Utilize multivariable risk prediction models for assessing individual risk. Implement prevention strategies focusing on lifestyle modifications. Ensure transparency in reporting and validation of risk models.
References