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

Determinants of interval cancer and tumor size among breast cancer screening participants

Breast cancer is the most common cancer of women in Sweden and globally. In the more affluent countries, mammography screening has been in place for a few decades and has successfully reduced mortality. However, there is increasing interest in enhancing the impact of screening by going from the current age-based screening system to a risk-based system. There are two risk components that must be taken into account – the underlying breast cancer risk and the risk of delayed detection. Mammographic density, the amount of dense tissue in the breast, has been shown to be a risk factor for both. In this thesis, my aim was to identify novel determinants of delayed breast cancer detection by studying observed cases of interval cancer or large cancer at diagnosis. The potential risk factors for delayed detection were based on negative mammograms and other data that can be determined before diagnosis. Study I to III, were based on a retrospective case-only population, while Study IV was based on a prospective cohort. In Study I, we developed an estimate of the longitudinal fluctuation in mammographic percent density between screenings. Based on our results, we concluded that women that were subsequently diagnosed with interval cancer had higher density fluctuations than women with screen-detected cancer. In Study II, we went beyond density and examined 32 other image features which were computer-extracted from digitized mammograms. We identified two novel features that were associated with an increased risk of interval cancer compared to screen-detected cancer. One feature seemed to be related to the shape of the entire dense area, being flat rather than round increased the risk of interval cancer, possibly due to making clinical detection easier. The other feature seemed to be related to whether the density was more concentrated or instead was interspersed with fatty streaks. When density was more concentrated, the risk of interval cancer increased, possibly by making mammographic detection more difficult. In Study III, we determined risk factors for the cancer diagnosis being delayed until the cancer had reached a size larger than 2 cm. High density and high body mass index (BMI) were already known risk factors in general. Our aim was to understand if different factors were involved depending on the detection mode, screen-detection or interval cancer detection. We found that high BMI increased the risk of large cancer markedly among interval cancers and somewhat among screen-detected cancers. High density was associated with large cancer only among screen-detected cases. In survival analysis, we showed that high BMI increased the risk of disease progression, but only among women with interval cancer. In Study IV, we found that the localized density category at the site of the subsequent cancer was often different compared to the overall density. We examined the effect of high localized density, independent of overall density, and found that it was strongly associated with large cancer at diagnosis. In addition, it was associated with interval cancer among the less aggressive node-negative cases. It remains to be elucidated whether this effect is purely due to visual masking or also due to an association with biological characteristics of the tumor microenvironment. In conclusion, we have identified several novel determinants of delayed breast cancer detection, which could be further validated in trials of risk-stratified screening

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

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Mammographic density and breast cancer phenotypes

Mammographic density is one of the strongest risk factors for breast cancer and has been thoroughly studied as such. Extensive mammographic density also decreases screening sensitivity, thereby increasing the risk of interval cancers. Whether density acts as fertile ground for all types of breast cancer, or whether it influences tumor growth in a specific direction, was not known when we embarked upon the studies of this thesis. We therefore aimed to investigate the association between density, tumor characteristics, molecular subtypes, recurrence, and survival, focusing on interval cancers in the last study. For studies I, III, and IV, we used the cases included in a population-based case-control study, in which cases were all Swedish women, aged 50-74, with incident breast cancer, diagnosed 1993-1995 (n=3345). We only included postmenopausal women with no prior history of cancer other than non-melanoma skin cancer and cervical cancer in situ (n=2720). Of these women, 1774 women had eligible mammograms. For study II, in which we investigated the relationship between density and molecular subtypes, the study population was based on all women with breast cancer operated at a large university hospital in Stockholm 1994-1996 (n=524). Women with available gene expression profiling and mammograms were included in the study (n=110). Pre-diagnostic/diagnostic density of the unaffected breast was assessed using a semi-automated, computer-assisted thresholding technique, Cumulus. Density was either measured as the dense area in cm2 (absolute density=AD) or percentage density (PD) (the absolute dense area/the total breast area). We did not find an association between density and tumor characteristics (lymph node metastasis, hormone-receptor status, grade, and histopathological classification) except for tumor size. However, this association seemed at least in part to be due to masking delaying diagnosis. In accordance with the lack of association between PD and most tumor characteristics, we did not find an association between density and molecular subtypes, nor between density, distant recurrence, and survival. We did, however, see a relatively strong association between PD and both local and locoregional recurrence, independent of established risk factors. In the last study, we investigated the differences in survival between interval cancers and screening-detected cancers, taking mammographic density into account. We could show that interval cancers in both dense and non-dense breasts were associated with poorer prognosis compared to screening-detected cancers. However, the poorer prognosis seen in interval cancers in dense breasts seemed mainly attributable to delayed detection, whereas the group of interval cancers in non-dense breasts primarily seemed composed of truly aggressive tumors which we believe need further study

Diffuse Optical Tomography Imaging of Chemotherapy-Induced Changes in Breast Tissue Metabolism

Breast cancer is fast becoming the leading cause of mortality in women worldwide. As of this year, there are more than 3.1 million women with a history of breast cancer in the U.S., and about 41,760 women are expected to die from this disease. Neoadjuvant chemotherapy (NAC) has become a well-established therapy in the treatment of patients with locally advanced or primarily inoperable breast cancer. It consists of 3-9 months of drug treatment to shrink the tumor size before surgical removal of any remaining mass. A pathological complete response (pCR) is defined as complete disappearance of the tumor before surgery and correlates with 5-year overall survival of the treated patient. However, only 15-40% of subjects who undergo NAC will achieve a pCR, while the remaining patients do not benefit from a therapy that has considerable side effects. In this Ph.D. thesis, I explore the potential of diffuse optical tomography (DOT) for breast cancer imaging and NAC monitoring. The overall objective is two-fold. First, I seek to identify breast cancer patients who will not respond to NAC shortly after the initiation of a 5-9 months therapy regimen. Identifying these patients early will allow a switch to a more promising therapy and avoiding months of ineffective therapy with a drug regimen that has considerable side effects. Second, I use the optical data simultaneously obtained from the contralateral, non-tumor bearing breast to better understand the factors that modulate breast density and the source of its contrast in DOT. This work analyzed DOT data from 105 patients with stage II-III breast cancer under NAC regimen. Data processing and image analysis protocols were developed to more effectively evaluate static tissue contrast and dynamic functional imaging of the breast. Notably, we observed that there are differences in the time evolution of DOT features between pCR and non-pCR tumors under NAC, and DOT features can contribute to the successful prediction of pCR status from pretreatment imaging. Lastly, our analysis demonstrated a positive correlation between DOT feature and mammographic density classification, which could lead to research on the potential use of DOT as a predictor of breast cancer as well as an assessment tool to longitudinally evaluate the efficacy of chemoprevention strategies. These findings represent important steps towards the translation of DOT into current clinical workflow to contribute to better-personalized breast cancer therapies and breast cancer risk management