Objective:

To critically assess previously developed endometrial cancer (EC) risk prediction models by examining the populations studied, predictors used, and methodologies employed, focusing on their effectiveness and challenges in clinical application.

Key Findings:

• Endometrial cancer incidence is rising, particularly among women from diverse racial/ethnic backgrounds, highlighting the need for tailored risk assessment.
• Early detection significantly improves survival rates, yet no universal screening recommendations exist, indicating a gap in clinical practice.
• Multivariable predictive models have been developed to estimate individual risk, but their generalizability is limited by dataset composition and reporting transparency, which may hinder their clinical utility.

Interpretation:

Accurate identification of high-risk individuals through predictive models can enhance targeted interventions, but existing models face challenges in validation and applicability across diverse populations, necessitating further research.

Limitations:

• Variability in model performance metrics and methodologies across studies may lead to inconsistent findings.
• Limited generalizability due to cohort composition and lack of transparency in reporting, which could affect the applicability of models in diverse settings.
• Exclusion of certain model types may overlook relevant predictive insights, potentially limiting the scope of risk assessment.

Conclusion:

Improving the validation and transparency of EC risk prediction models is essential for effective prevention and early detection strategies, particularly to address inequities in EC outcomes and ensure all populations benefit from advancements in predictive modeling.