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

Abstract

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. 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.