Breast cancer risk associated with changes in mammographic density.

PhD ThesisBreast cancer is the most common cancer in the UK, and mammographic density (‘density’) is one of its strongest known risk factors. At present, most research focuses on static measures of density to determine population effects. The central hypothesis of this thesis is that repeated measures of density are more valuable for personalised breast cancer prevention. This hypothesis was tested through the following research. Study-I investigated within-women associations between body mass index (BMI) and density, to assess whether density (visual/Cumulus/volumetric ‘Stepwedge’) acts as a mediator for breast cancer risk reduction during a premenopausal weight-loss intervention (n=65). Study-II evaluated the benefit of using a woman’s longitudinal history of (BI-RADS) density to improve breast cancer risk estimation (n=132,439). Study-III was a Cochrane systematic review investigating the association between endocrine therapy-induced density reduction and breast cancer risk and mortality. Studies-IV and V (n=575) evaluated visually-assessed density reduction with prophylactic anastrozole during the International Breast Cancer Intervention Study-II, and its use as a biomarker for concurrent breast cancer risk reduction, respectively. In Study-I, change in BMI was associated with change in breast fat but not dense tissue, negating density reduction as a biomarker for risk reduction with weight-loss. In Study-II, longitudinal density provided approximately a quarter more statistical information than most recent density and improved discriminatory accuracy. Study-III found evidence that density reduction may be a biomarker for reduction in risk and mortality with tamoxifen, but the level of evidence was limited by some study quality issues. Study-IV indicated that preventive anastrozole might marginally reduce density, but statistical significance was not obtained. In Study-V, sample size was too small to draw definitive conclusions. Overall, changes in density were useful for the study of breast cancer risk and should be considered for personalised breast cancer prevention strategies

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Risk assessment and prevention of breast cancer

One woman in eight develops breast cancer during her lifetime in the Western world. Measures are warranted to reduce mortality and to prevent breast cancer. Mammography screening reduces mortality by early detection. However, approximately one fourth of the women who develop breast cancer are diagnosed within two years after a negative screen. There is a need to identify the short-term risk of these women to better guide clinical followup. Another drawback of mammography screening is that it focuses on early detection only and not on breast cancer prevention. Today, it is known that women attending screening can be stratified into high and low risk of breast cancer. Women at high risk could be offered preventive measures such as low-dose tamoxifen to reduce breast cancer incidence. Women at low risk do not benefit from screening and could be offered less frequent screening. In study I, we developed and validated the mammographic density measurement tool STRATUS to enable mammogram resources at hospitals for large scale epidemiological studies on risk, masking, and therapy response in relation to breast cancer. STRATUS showed similar measurement results on different types of mammograms at different hospitals. Longitudinal studies on mammographic density could also be analysed more accurate with less nonbiological variability. In study II, we developed and validated a short-term risk model based on mammographic features (mammographic density, microcalcifications, masses) and differences in occurrences of mammographic features between left and right breasts. The model could optionally be expanded with lifestyle factors, family history of breast cancer, and genetic determinants. Based on the results, we showed that among women with a negative mammography screen, the short-term risk tool was suitable to identify women that developed breast cancer before or at next screening. We also showed that traditional long-term risk models were less suitable to identify the women who in a short time-period after risk assessment were diagnosed with breast cancer. In study III, we performed a phase II trial to identify the lowest dose of tamoxifen that could reduce mammographic density, an early marker for reduced breast cancer risk, to the same extent as standard 20 mg dose but cause less side-effects. We identified 2.5 mg tamoxifen to be non-inferior for reducing mammographic density. The women who used 2.5 mg tamoxifen also reported approximately 50% less severe vasomotor side-effects. In study IV, we investigated the use of low-dose tamoxifen for an additional clinical use case to increase screening sensitivity through its effect on reducing mammographic density. It was shown that 24% of the interval cancers have a potential to be detected at prior screen. In conclusion, tools were developed for assessing mammographic density and breast cancer risk. In addition, two low-dose tamoxifen concepts were developed for breast cancer prevention and improved screening sensitivity. Clinical prospective validation is further needed for the risk assessment tool and the low-dose tamoxifen concepts for the use in breast cancer prevention and for reducing breast cancer mortality

Breast cancer natural history models and risk prediction in mammography screening cohorts

In this thesis, the foundations are laid for a new natural history model for breast cancer—specifically designed to take advantage of detailed screening cohorts. Three diverse applications of this model are then presented. Study I develops the statistical framework for the natural history model, and shows with simulations that the model parameters can be estimated based on only the information available at diagnosis. It also describes how to adjust for random left truncation—an important aspect to consider when studying prospective cohorts. In Study II, the newly developed natural history model is applied to a Swedish mammography screening cohort. It estimates the population-level distributions of age at onset and tumor volume doubling time. As an extension, the tumor volume doubling time is allowed to depend on the age at onset. The study also estimates the rate of symptomatic detection and screening sensitivity as functions of tumor size. Simulations are used to validate the estimates. Study III shifts the focus from inference to risk prediction. The natural history model is modified to incorporate risk factors separately in each of the four components of the model. Short-term risk prediction is then performed on the screening cohort and the results are compared to a conventional approach to breast cancer risk prediction. The study also develops novel predictions based on, for example, having experienced tumor onset, having a tumor detected at the next screening, and having a tumor detected before it reaches a certain size if attending the next screening. In Study IV, the model is used to study the effect that certain acquisition parameters used in mammography have on the detectability of the breast cancer tumor. With the model, it is possible to more directly study the mammography screening sensitivity, compared to the ad hoc definition of sensitivity commonly seen in the screening literature. It was identified that the compressed breast thickness—in addition to the percent mammographic density and latent tumor size—was inversely associated with the screening sensitivity

PB.23: Effect of detector type on cancer detection in digital mammography

This work measured the effect that image quality associated with different detectors has on cancer detection in mammography using a novel method for changing the appearance of images.\ud \ud A set of 270 mammography cases (one view, both breasts) was acquired using five Hologic Selenias and two Hologic Dimensions X-ray units: 80 normal, 80 with simulated inserted subtle calcification clusters, 80 with subtle real noncalcification malignant lesions and 30 with benign lesions (biopsy proven). These 270 cases (Arm 1) were converted to appear as if they had been acquired on two other imaging systems: needle image plate computed radiography (CR) (Arm 2) and powder phosphor CR (Arm 3). Three experienced mammography readers marked the location of suspected cancers in the images and classified whether each lesion would require further investigation and the confidence in that decision. Performance was calculated as the area under curve (AUC) of the alternative free-response receiver operating characteristic curv