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INTRODUCTION: To evaluate the performance of QCancer® (Renal) for predicting the absolute risk of renal tract cancer in a large independent UK cohort of patients from general practice records. MATERIALS AND METHODS: Open cohort study to validate QCancer® (Renal) prediction model. Record from 365 practices from United Kingdom contributing to The Health Improvement Network (THIN) database. 2.1 million patients registered with a general practice surgery between 01 January 2000 and 30 June 2008, aged 30-84 years (3.7 million person years) with 2283 renal tract cancer cases. Renal tract cancer was defined as incident diagnosis of renal tract cancer during the 2 years after study entry. Model discrimination was measured using the receiver operating characteristics derived area under the curve. Calibration plots examined the relationship between predicted and observed probabilities of undetected renal tract cancer. RESULTS: The results from this independent and external validation of QCancer® (Renal) demonstrated good performance data on a large cohort of general practice patients. QCancer® (Renal) had very good discrimination with areas under the ROC curve of 0.92 and 0.95 for women and men respectively. QCancer® (Renal) was well calibrated across all tenths of risk and over all age ranges with predicted risks closely matching observed risks. QCancer® (Renal) explained 74.4% and 74.2% of the variation in men and women respectively. A limitation of our study is the recording of symptoms might be less complete, as patients with mild symptoms may not visit their general practitioner or not report mild symptoms. CONCLUSIONS: QCancer® (Renal) are useful tools to help in identifying undetected cases of undiagnosed renal tract cancer in primary care in the UK.

Original publication




Journal article


Cancer epidemiol

Publication Date





115 - 120


Adult, Aged, Aged, 80 and over, Cohort Studies, Delayed Diagnosis, Early Detection of Cancer, Female, Follow-Up Studies, General Practice, Humans, Kidney Neoplasms, Male, Middle Aged, Models, Statistical, Predictive Value of Tests, Primary Health Care, Risk Assessment, United Kingdom