Predicting Risk Of Cancer At Screening (PROCAS): results on the first 10,000 women entered from the Greater Manchester National Breast Screening Programme (NHSBSP)
Year: 2010
Session type: Poster / e-Poster / Silent Theatre session
Background
Currently the NHS does not identify women at increased risk of breast cancer in the NHSBSP. The PROCAS study aims to determine breast cancer risk in 60,000 women based on a combination of standard risk factors, mammographic density and relevant single nucleotide polymorphisms (SNPs). This development aims to improve risk estimation and consider using the information to counsel women at high risk and potentially adapt screening interval based on personal risk. Here we report on the first 10,000 women entered.
Method
Women invited for mammography are also invited to PROCAS. This involves completion of risk factor questionnaire and providing a saliva sample for SNP testing. Several methods of estimating mammographic density are being assessed, including visual analogue scale (VAS). Women at high risk (?8% 10year risk or ?5% 10-year risk and ?60% mammographic density [VAS]) are invited for clinical review in our Family History Clinic (FHC). We predict 600 cancers in the total population which will enable us to evaluate the utility of predictive risk algorithms.
Results
Between 11/09-07/10; 44,711 women were invited to attend screening . 67.7% attended screening of which 35% entered PROCAS. Mean mammographic density (VAS) on 9121 women was 29%,7.6% had >59% density. Ten year risk of breast cancer, based on the Tyrer-Cuzick model, ranged from <1% to 15% (median 2.5%). 93% of women wished to know their breast cancer risk. To date, 90 women have been estimated to be at high risk and thus far 68 have been counselled by FHC clinicians. Updated results will be reported.
Conclusion
These initial data indicate that risk determination and counselling within the NHSBSP/FHC organisation is feasible. Data on the projected 60,000 women will enable us to assess whether or not: combining risk factors will improve prediction; fully automated data collection and reporting of risk in this manner is workable within NHSBSP.