Diagnostic prediction models for ovarian cancer in primary care (Ovatools): an external validation study

Start Date Jan 2022

Code C17-C

Status Ongoing

Project Lead
Senior Lead
Prof Stephen Duffy, Prof Borislava Mihaylova (both QMUL)


Ovarian cancer is the 6th most common cause of death from cancer in UK women. Most ovarian cancers are not picked up until the disease is advanced when it is harder to treat. Earlier diagnosis may improve outcomes including survival but this remains challenging. The majority of women with ovarian cancer are diagnosed after visiting their general practitioner (GP) with possible symptoms of the disease. However, these symptoms are often non-specific and it can be difficult to determine which women have ovarian cancer and should be referred urgently to a specialist and which women can be reassured. A blood test – Cancer Antigen 125 (CA125) – is currently used by GPs to investigate women with symptoms of possible ovarian cancer. We recently developed models (Ovatools) which predict the probability of ovarian cancer and all cancers based on a woman’s CA125 level and age, in those being tested in General Practice. These could be used to help make individual informed decisions about the need for further investigation and could help inform guidelines on cancer detection. Before using these models in clinical practice, it is important to ensure their reliability.

In this study, we will use anonymised primary care, hospital and cancer registry data from women who have had a CA125 test in English general practice to evaluate the Ovatools models and determine how well they perform. We will also consider the health economic implications of implementing these models within the diagnostic pathway.

Outputs & impact

If the Ovatools models prove reliable on external validation and are cost effective, they could be used within primary care to help determine which women need urgent cancer referral for further investigation and which women can be reassured. This could help ensure that women at greatest risk of cancer undergo prompt investigation, diagnosis and treatment, while minimising unnecessary invasive investigations in women at low risk of the disease.

Related projects

Tests and tools for the detection of ovarian cancer in primary care (PhD)

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