Womb (endometrial) cancer is the fourth most common female cancer. The number of cases is rising quickly, probably because of increasing levels of obesity, with which it is strongly associated. Being diagnosed with womb cancer is not only upsetting, but treatment can be unpleasant and even dangerous for some. When found early, womb cancer can be cured with a hysterectomy (surgical removal of the womb) but when it has spread, the outlook is very poor.
Whilst preventing womb cancer appears logical, there are no tried and tested interventions to stop womb cancer developing and no means of recognising which women would benefit most from such a strategy.
Our group have been developing models using routinely collected data aimed at women aged 45-60 years to ascertain those at high risk of womb cancer. We wish to check the working of the models within the CPRD to ensure that it accurately categorises women and is ready for use in general practice. The best performing model will subsequently be used to guide entry into trials of different strategies to try to prevent womb cancer, ensuring women at low risk of womb cancer avoid unnecessary intervention. Ultimately, it is hoped that one of the developed models will be used by GPs to determine a woman’s risk of developing womb cancer, with interventions offered to those at high risk. Such an approach could reduce the number of womb cancers diagnosed annually in the UK by up to 50%, saving over 1000 lives each year.
This project aims to develop an accurate endometrial cancer risk prediction model to be used in primary care and to identify potential prevention strategies for investigation in future clinical trials. We aim to use the CPRD to externally validate our flexible parametric survival and neural network models that we will develop separately, which utilise age, body mass, reproductive and family history and measures of insulin resistance to quantify an individual woman’s endometrial cancer risk. The CPRD has been specifically chosen for model validation as it contains data on a patient cohort representative of the general population. The dataset will be restricted to female patients aged 45-60 years registered with either a CPRD Gold or Aurum practice between 1/1/2000 and 31/12/2016 and eligible for linkage to HES APC, NCRAS and ONS mortality records. HES data will be used to determine diagnoses and operations and combined with cancer registry data to ascertain endometrial cancer cases. Validation of the models will be performed by applying the prediction model (exactly as derived) to women with an intact uterus, with calibration quantified through flexible calibration plots (plotting the observed number of events against predicted risks for groups across the risk prediction spectrum) and estimation of calibration intercept and slope. The ability of the models to discriminate at baseline between individuals who do and do not go on to develop endometrial cancer will be assessed using the concordance (c) statistic. Should the models perform poorly at external validation, the coefficients will be updated using data from new individuals within the CPRD.
A multivariable logistic regression model, adjusting for age, body mass and other important risk factors, will be used to estimate the efficacy of potential prevention strategies. These include those interventions identified in our systematic review; namely, the Mirena intrauterine system, metformin, aspirin and weight loss.
Endometrial cancer diagnosis; endometrial cancer histological sub-type
Sarah Kitson - Chief Investigator - University of Manchester
Sarah Kitson - Corresponding Applicant - University of Manchester
Annette Payne - Collaborator - Brunel University London
Artitaya Lophatananon - Collaborator - University of Manchester
Darren Ashcroft - Collaborator - University of Manchester
Emma Crosbie - Collaborator - University of Manchester
Emmanouil Karteris - Collaborator - Brunel University London
Evangelos Kontopantelis - Collaborator - University of Manchester
Glen Martin - Collaborator - University of Manchester
Helena O'Flynn - Collaborator - University of Manchester
Jayanta Chatterjee - Collaborator - Brunel University London
Rebecca Karkia - Collaborator - Brunel University London
Rebecca Karkia - Collaborator - Brunel University London
Annette Payne - Collaborator - Brunel University London
Jayanta Chatterjee - Collaborator - Brunel University London
Emmanouil Karteris - Collaborator - Brunel University London
HES Admitted Patient Care;NCRAS Cancer Registration Data;No additional NCRAS data required;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation