| dc.description.abstract | Mammographic density, the amount of radiodense tissue on a mammogram, is a strong risk factor for breast cancer, with properties that could be an asset in screening and prevention
programmes. Its use in risk prediction contexts is currently limited, however,
mainly due to di culties in measuring and interpreting density.
This research investigates rstly, the properties of density as an independent marker of
breast cancer risk and secondly, how density should be measured.
The rst question was addressed by analysing data from a chemoprevention trial, a trial
of hormonal treatment, and a cohort study of women with a family history of breast
cancer . Tamoxifen-induced density reduction was observed to be a good predictor of
breast cancer risk reduction in high-risk una ected subjects. Density and its changes
did not predict risk or treatment outcome in subjects with a primary invasive breast
tumour. Finally absolute density predicted risk better than percent density and showed
a potential to improve existing risk-prediction models, even in a population at enhanced
familial risk of breast cancer.
The second part of thesis focuses on density measurement and in particular evaluates
two fully-automated volumetric methods, Quantra and Volpara. These two methods
are highly correlated and in both cases absolute density (cm3) discriminated cases from
controls better than percent density. Finally, we evaluated and compared di erent measurement
methods. Our ndings suggested good reliability of the Cumulus and visual
assessments. Quantra volumetric estimates appeared negligibly a ected by measurement
error, but were less variable than visual bi-dimensional ones, a ecting their ability
to discriminate cases from controls. Overall, visual assessments showed the strongest
association with breast cancer risk in comparison to computerised methods.
Our research supports the hypothesis that density should have a role in personalising
screening programs and risk management. Volumetric density measuring methods,
though promising, could be improved. | en_US |
