Fully-automated techniques are expected to be the future of measuring mammographic density, since they will hopefully provide predictive ability as regards breast cancer risk and quick assessments, would be possible to implement on a population scale and would
Review of breast density issues and study plan 46
ensure reproducibility of the results. In addition to this, while repeatability and repro- ducibility are important in measurement of density, as with measurement of any other attribute, the crucial feature is the ability to predict breast cancer risk. Therefore a number of technologies are currently being developed or undergoing validation.
Quantra [51,178] and Volpara [52,179] are two of such methods in need of evaluation, before being recommended in routine screening. In this part of the thesis data from Princess Grace Hospital, a private facility in central London, are analysed to investigate the properties of the two new techniques, comparing their predictive ability. In order to clarify the novelty element of this thesis, Table1.2 compares the published results from these studies and the issues that will be addressed in this work.
Review of breast density issues and study plan 47
Table 1.2: Main published findings of the studies analysed in part II and new issues addressed in this thesis
Published
Chapter Results Unreported issues for analyses in this thesis Data
Chapter 5 - Analyses of the association between Case-control breast cancer risk and Quantra absolute study and relative density measures, also N=400 in comparison to BIRADS visual (200 cases) assessments.
Evaluation of the impact of age and area of residence on such association.
Chapter 6 - Assessment of changes in breast Longitudinal composition as estimated by Quantra, dataset according to age and area of N=332
residence. (no cases)
Chapter 7 - Analyses of the association between Case-control breast cancer risk and Volpara absolute study and relative density measures, also N=366 in comparison to Quantra and BIRADS. (182 cases) Evaluation of the impact of age and
area of residence on such association.
Chapter 8:
1. IBIS-I see Evaluation of intra- and inter-reader see Table 1.1
Table 1.1 agreement in Cumulus and visual (21 agreement.
2. Chapters5 - Assessment of potential measurement see above and 6 error in Quantra density estimates and
its impact on their association with breast cancer risk.
Evaluating the relationship between volumetric and area density assessments.
Review of breast density issues and study plan 48
1.3.2.1 A case-control evaluation of a fully automated volumetric density measure as a predictor of breast cancer risk (1) (Quantra),
Chapter 5
Data As previously noted, to be practically useful in the context of population screen- ing and risk management, automatic measurement of density, with little or no call on manpower, is desirable. In this study details were recorded of 200 cases, female patients with histopathologically verified breast cancer, and 200 matched controls. For every subject, mammographic density was assessed both with Quantra volumetric measures of absolute and relative density and visually with the BIRADS classification. The avail- able covariates also included age, area of residence and the Quantra measure of total breast volume.
Proposed analysis
• To compare Quantra measures of absolute and relative volumetric density as re- gards their ability to discriminate between cases and controls.
• To examine the relationship between mammographic density, as assessed with Quantra, and the established BIRADS method.
• To assess how the association between density estimates and breast cancer risk varies according to age and area of residence.
1.3.2.2 Serial volumetric density measures using Quantra, Chapter 6
Data This dataset recorded details of 332 women, (231 younger than 50 years and 101 aged 50 or more years) undergoing two mammographic examinations, typically 18 months apart. Density was assessed with Quantra and we also had information on age and area of residence.
Review of breast density issues and study plan 49
Proposed analysis
• To assess changes in breast composition (i.e. amount of dense and fat tissue) over time, for the combined study group and each age cohort separately.
1.3.2.3 A case-control evaluation of a fully automated volumetric density measure as a predictor of breast cancer risk (2) (Volpara),
Chapter 7
Data The mammograms of 366 women (182 cases and 184 controls) from the Quantra case-control study, were reassessed using Volpara[52,179], another fully-automated vol- umetric measure.
Proposed analysis
• To examine the relationship between the mammographic density measures pro- duced with Volpara and the established BIRADS method.
• To compare Volpara measures of absolute and relative volumetric density as re- gards their ability to discriminate between cases and controls.
• To assess how the association between density estimates and breast cancer risk varies according to age and area of residence.
• To compare directly Volpara and Quantra assessments.
1.3.2.4 Measuring mammographic density: results, issues and potential im- plications, Chapter 8
This chapter includes analyses on data described in Chapters2,4,5and6and it focuses on variability from measurement error and population variation in breast composition.
Proposed analysis
• To evaluate inter- and intra-reader agreement in Cumulus [28] and visual (21- categories) assessments using data from the IBIS-I study [17], described in Chapter
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• To investigate how measurement error in density assessed with a fully-automated volumetric method (Quantra) may influence the association between density and breast cancer risk, observed in Chapter5.
• To adjust the association between risk and Quantra absolute density estimates (Chapter5) for the potential measurement error, using data introduced in Chapter
6 to estimate variability components.
• To study the relationship between two- and three-dimensional density assessments, and their difference in variability, using data from the longitudinal study presented in Chapter 6 and CADET1 study [4].
• To compare the propensity for risk prediction of most of the density measures available for this project (visual 21-category assessment from Chapter2, Cumulus from Chapters 2 and 4, Quantra from Chapter 5 and Volpara from Chapter 7) using standardised odds ratios and the areas under the ROC curve (AUCs).