Identifying women with undetected ovarian cancer: independent and external validation of QCancer(®) (Ovarian) prediction model.
Nov 2012
Source
Centre for Statistics in Medicine, Wolfson College Annexe, University of Oxford, Oxford, UK.
Abstract
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.
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