Ovarian cancer prediction: development of a scoring system for primary care


Correspondence: Dr K Grewal, Academic Foundation Year 2, University Hospital Bristol, Bristol Royal Infirmary, Upper Maudlin Street, Bristol BS2 8HW, UK. Email grewal.karen86@gmail.com



Recent studies have identified specific symptoms of ovarian cancer at all stages, raising the hope of reducing diagnostic delays. We aimed to devise a scoring system for symptoms of ovarian cancer in primary care.


Secondary analysis of data from a case–control study.


Thirty-nine general practices in Exeter, mid-Devon and east Devon.


Two hundred and twelve women with ovarian cancer and 1060 age-, sex- and practice-matched controls.


Conditional logistic regression was used to produce an additive scoring system and its receiver operator characteristic (ROC) curve. Several different cut-offs were then tested using a simple costs model.

Main outcome measures

The ROC curve value.


Each woman was assigned a score based on her symptoms in the year before diagnosis: we added a score for women aged ≥50 years, reflecting their increased incidence of ovarian cancer. The area under the ROC curve was 0.883 (95% confidence interval 0.853–0.912). The chosen cut-off had a sensitivity of 72.6% and a specificity of 91.3%.


This scoring system could potentially direct general practitioners to appropriate investigations for ovarian cancer on the basis of symptoms and save a substantial number of unnecessary ultrasound scans being requested.