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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:655178 |
| Date | January 2014 |
| Creators | Assi, Valentina |
| Publisher | Queen Mary, University of London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://qmro.qmul.ac.uk/xmlui/handle/123456789/7855 |
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