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

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Aromatase inhibitor-induced modulation of breast density: clinical and genetic effects

Background: Change in breast density may predict outcome of women receiving adjuvant hormone therapy for breast cancer. We performed a prospective clinical trial to evaluate the impact of inherited variants in genes involved in oestrogen metabolism and signalling on change in mammographic percent density (MPD) with aromatase inhibitor (AI) therapy. Methods: Postmenopausal women with breast cancer who were initiating adjuvant AI therapy were enrolled onto a multicentre, randomised clinical trial of exemestane vs letrozole, designed to identify associations between AI-induced change in MPD and single-nucleotide polymorphisms in candidate genes. Subjects underwent unilateral craniocaudal mammography before and following 24 months of treatment. Results: Of the 503 enrolled subjects, 259 had both paired mammograms at baseline and following 24 months of treatment and evaluable DNA. We observed a statistically significant decrease in mean MPD from 17.1 to 15.1% (P<0.001), more pronounced in women with baseline MPD ⩾20%. No AI-specific difference in change in MPD was identified. No significant associations between change in MPD and inherited genetic variants were observed. Conclusion: Subjects with higher baseline MPD had a greater average decrease in MPD with AI therapy. There does not appear to be a substantial effect of inherited variants in biologically selected candidate genes

Mammographic density. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations

Artificial intelligence in mammographic phenotyping of breast cancer risk: A narrative review

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman\u27s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies

Comparison between two packages for pectoral muscle removal on mammographic images

Background: Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. Purpose: To compare performance of the two packages on a single database of FFDM images. Methods: Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA’s and OpenBreast’s removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal–Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. Results: FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA’s Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P &lt; 0.05). No dependence on breast density or laterality has been found (Kruskal–Wallis test P &gt; 0.05). Conclusion: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation

Diagnostic Reference Levels for digital mammography in Australia

Aims: In 3 phases, this thesis explores: radiation doses delivered to women during mammography, methods to estimate mean glandular dose (MGD), and the use of mammographic breast density (MBD) in MGD calculations. Firstly, it examines Diagnostic reference levels (DRLs) for digital mammography in Australia, with novel focus on the use of compressed breast thickness (CBT) and detector technologies as a guide when determining patient derived DRLs. Secondly, it analyses the agreement between Organ Dose estimated by different digital mammography units and calculated MGD for clinical data. Thirdly, it explores the novel use of MBD in MGD calculations, suggesting a new dose estimation called the actual glandular dose (AGD), and compares MGD to AGD. Methods: DICOM headers were extracted from 52405 anonymised mammograms using 3rd party software. Exposure and QA information were utilised to calculate MGD using 3 methods. LIBRA software was used to estimate MBD for 31097 mammograms. Median, 75th and 95th percentiles were calculated across MGDs obtained for all included data and according to 9 CBT ranges, average population CBT, and for 3 detector technologies. The significance of the differences, correlations, and agreement between MGDs for different CBT ranges, calculation methods, and different density estimation methods were analysed. Conclusions: This thesis have recommended DRLs for mammography in Australia, it shows that MGD is dependent upon CBT and detector technology, hence DRLs were presented as a table for different CBTs and detectors. The work also shows that Organ Doses reported by vendors vary from that calculated using established methodologies. Data produced also show that the use of MGD calculated using standardised glandularities underestimates dose at lower CBTs compared to AGD by up to 10%, hence, underestimating radiation risk. Finally, AGD was proposed; it considers differences in breast composition for individualised radiation-induced risk assessment

Maximum-Likelihood Estimation of Glandular Fraction for Mammography and its Effect on Microcalcification Detection

Objective: Breast tissue is a mixture of adipose and fibro-glandular tissue. The risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists need to know quantitative glandular fraction when diagnosing a patient. Another increasingly popular mammography protocol is eliminating the anti-scatter grid and using software algorithms to reduce scatter. This work uses a Maximum Likelihood Expectation Maximization algorithm to estimate the pixel-wise glandular fraction from images taken with an anti-scatter grid or with scatter removed algorithmically. The work also studies if presenting the pixel-wise glandular fraction image alongside the usual mammography image has the potential to improve micro-calcification detection. Approach: The algorithms are implemented and evaluated with TOPAS Geant4-generated images with known glandular fractions. These images are also taken with and without microcalcifications present to study the effects of GF-estimation on microcalcification detection. We then applied the algorithm to a few clinical DICOM images with and without microcalcifications. Results: For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 3.2% and 2.5% for the without and with anti-scatter grid cases. Results from DICOM clinical images (where the proper glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in DICOM images and 5.1-88% in TOPAS images. Significance: This work studied the accuracy of maximum likelihood estimation for a glandular fraction on simulated and clinical images and shows an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer.Comment: Manuscipt under peer-revie