Identifying women with undetected ovarian cancer: Independent and external validation of QCancer^® (Ovarian) prediction model

Early identification of ovarian cancer is an unresolved challenge and the predictive value of single symptoms is limited. We evaluated the performance of QCancer^® (Ovarian) prediction model for predicting the risk of ovarian cancer in a UK cohort of general practice patients. A total of 1.1 million patients registered with a general practice surgery between 1 January 2000 and 30 June 2008, aged 30-84 years with 735 ovarian cancer cases, were included in the analysis. Ovarian cancer was defined as incident diagnosis of ovarian cancer during the 2 years after study entry. The results from this independent and external validation of QCancer^® (Ovarian) prediction model demonstrated good performance on a large cohort of general practice patients. QCancer^® (Ovarian) had very good discrimination with an area under the receiver operating characteristic curve of 0.86 and explained 59.9% of the variation. QCancer^® (Ovarian) was well calibrated across all tenths of risk and over all age. The 10% of women with the highest predicted risks included 64% of all ovarian cancer diagnoses over the next 2 years. QCancer^® (Ovarian) appears to be a useful tool for identifying undetected cases of ovarian cancer in primary care in the UK for early referral and investigation.