Introduction/Background Global endometrial cancer (EC) cases continue to increase, placing a significant health and financial burden on individuals and healthcare services. Effective primary disease prevention strategies are urgently required but remain under-researched. Identifying high-risk women for intervention would ensure therapies are targeted at those most likely to benefit. This study aimed to develop a well calibrated EC risk prediction model based on routinely collected data and to validate it in an independent cohort.

Methodology Data from the UK Biobank, comprising 222,031 females ages 45–60 years and 902 incident EC cases, were used to build a flexible parametric survival model using EC risk factors identified through a systematic review of the literature. Model fit was improved with variable transformation and stepwise backward selection. Missing data were dealt with using multiple imputation and bootstrapping (100-fold) was applied for internal validation. Model calibration was assessed using flexible calibration plots and discrimination through calculation of the C-statistic. The model is being externally validated in the Clinical Practice Research Datalink, using data from 3,094,371 women, of whom 20,882 have developed EC.

Results Age, body mass index, waist circumference, age at menarche, age at last birth, late menopause (≥55 years), current hormone replacement therapy or tamoxifen use, prolonged oral contraceptive pill use (≥5 years), type 2 diabetes, smoking and family history of bowel cancer were incorporated into the model. Based on these variables, the model had an adjusted C-static of 0.75 and was well calibrated, with a calibration slope of 0.97 after internal validation.

Conclusion Our model, using easily measurable anthropometric, lifestyle and reproductive variables alongside personal and family medical history, accurately identifies women at high-risk of EC. External validation will determine whether it can be used to determine eligibility for primary EC prevention trials and reduce the size and costs associated with such studies.