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The focus of this study was to explore women’s experiences with follow-up after an abnormal mammogram, and factors that influence follow-up. Factors, including health status, found in the cancer screening and treatment literature, are necessary in identifying variables which have the potential to affect a person’s perception, and promote or deter follow-up. Protection Motivation Theory constructs utilized in this study are found in the literature to improve diagnostic health behaviors such as performing breast self-examination and complying with diagnostic tests. A non-experimental, descriptive, cross-sectional design was used to identify the barriers to follow-up after an abnormal mammogram by: 1) determining the noncompliance rate of follow-up mammograms among women screened at an urban hospital’s mammography mobile unit in North Texas (October 1, 2004, to September 31, 2005) who were found to need further evaluation for suspected abnormal findings; and 2) identifying factors associated with noncompliance and perceived barriers to noncompliance. The sample consisted of 262 participants, 136 (52%) women whom the hospital reported had not returned for follow-up and 126 (48%) women who were reported to have returned. A logistic regression model was performed using follow-up as the dependent variable. The variables most related to follow-up were (1) number of mammograms in the last 5 years; (2) having health insurance; (3) having problems receiving abnormal mammogram results; (4) having problems receiving or making a follow-up appointment; (5) taking off from work for the follow-up appointment; (6) not having transportation to follow-up appointment; and (7) waiting a long time to receive the follow-up appointment. Non-compliance to recommended follow-up after an abnormal mammogram is a serious public health concern, since breast cancer screening can improve breast cancer outcomes only if prompt diagnostic resolution and access to state-of-the-art care is available to all screening participants. This study adds to the literature on predictors of follow-up after an abnormal mammogram, as well as the to the health disparities literature.

Disparities exist in the health-seeking behavior of African American (AA) women in the United States. Specifically, AA women 40 years and older often do not adhere to guidelines for breast cancer screening because of demographic and socioeconomic factors that have impacted health disparities. The purpose of this study was to research negative health-seeking behavior toward early-stage breast cancer detection in AA women 40 years and older. The main research questions addressed whether there is a relationship between negative health-seeking behavior, operationally defined as lack of a personal doctor, lack of health insurance, and lack of doctor visits within the past 12 months, and early-stage breast cancer detection, operationally defined as lack of mammogram screening within the past 2 years, in AA women 40 years and older. This quantitative study was guided by the health belief model. A cross-sectional design was used along with secondary data from the 2016 Behavioral Risk Factor Surveillance System survey. Wald chi-square was used to examine the relationship between the dependent variables and the independent variable. The relationship between lack of a personal doctor, lack of health insurance, lack of doctor visits within the past 12 months, and lack of mammogram screening within the past 2 years was statistically significant at p < .05. The findings based on the significance between the variables confirmed that negative health- seeking behavior affects early-stage breast cancer detection in AA women 40 years and older. The results of this study may inform the development of educational programs that are instrumental in promoting and improving mammogram screening and early-stage breast cancer detection among AA women age 40 years and older.

Breast cancer is one of the leading causes of death and disability globally. Although mammogram has been identified as a significant breast screening tool in the United States, researchers have indicated that African-born women in the United States are diagnosed with advanced stages of breast cancer because of underutilization of mammogram from diverse reasons. The purpose of this quantitative study was to determine the association of demographic factors, breast cancer knowledge, health beliefs, and the utilization of mammogram among Nigerian women, 40 years and older in the United States (N=200). The study was guided by the health belief model and questionnaire was the data collection instrument used. Logistic regression analysis revealed that demographic variables, specifically age and length of residency in the United States have statistically significant effect on the odds of utilization of mammogram among the Nigerian women in the U.S. at p < 0.05. Also, according to the study results, breast cancer knowledge has a statistically significant effect on the utilization of mammogram at p <0.05. Additionally, health beliefs regarding breast cancer have significant effect on utilization of mammogram among Nigerian women 40 years and older in the U.S at p <0.05. The study findings will help in developing breast health programs for immigrant women, especially Nigerians in the U.S. to make informed decisions about timely utilization of mammographic services. Furthermore, the outcome of this study could enhance research, enlighten the health providers, and policymakers to develop culture sensitive preventive breast health programs that are appropriate to diverse women populations in the United States.

In current mammography practice, radiologists typically view mammograms in a symmetric, side-by-side, configuration in the belief that abnormalities will be made salient because they break the perceived symmetry. The literature on the use of symmetry as an aid to signal detection is limited and this thesis has taken a psychophysical approach to investigate the radiologist’s task of detecting a small mass (a blob) in paired mammogram backgrounds. Initial experiments used Gaussian white noise and synthetic mammogram backgrounds to test observer performance for the radiologist’s task using symmetric (side-by-side) displays and animated (the two images of a pair alternated sequentially in the same location) displays. The use of animated displays was then tested using real mammogram backgrounds in the subsequent experiments. The results showed that side-by-side presentation of paired images does not provide any benefit for the detection of a blob, whereas, alternated presentation enabled the observer to use the correlation present between the paired images to improve detection performance. The effect of alternation was not evident when applied to the task of detecting a small mass in real mammogram pairs and subsequent investigation suggested that the loss of effect resulted from the lack of scale invariance of real images. This meant that, regardless of the level of global correlation between two images, the localised correlation, at a region size reflecting the visual angle subtended by the fovea, was much lower. Thus, decorrelation by the visual system was ineffective and performance for the detection of a blob in the paired images was also ineffective. This thesis suggests that, whilst animated displays can be a powerful tool for the identification of differences between paired images, the underpinning mechanism of decorrelation makes them unsuited for mammograms where scale invariance means that correlation at local levels is a fraction of the global correlation level.

Sarcoidosis is a multisystem, inflammatory granulomatous disease that rarely involves breast tissue. The pathophysiology of this chronic granulomatous condition is not well understood but is thought to be multifactorial, involving environmental influences causing an amplified immune response. A key histomorphology feature in sarcoidosis is the presence of non-necrotizing granulomas. In this case, we report a 41-year-old African-American man with a known history of sarcoidosis of the lung who presented with gynecomastia and bilateral breast tenderness with palpable nodules. Subsequent biopsy and microscopic examination of the breast nodules revealed diffuse involvement with non-necrotizing granulomas in both breasts. A final diagnosis of extensive sarcoidosis involving breast tissue was rendered after excluding other causes of non-necrotizing granulomas. The patient underwent a bilateral mastectomy to remove the breast nodules. This case discusses sarcoidosis involving an unusual site.

Den senaste utvecklingen av artificiell intelligens (AI) och djupinlärning (DL) har gjort bild- och funktionsmedicin till en högst trolig kandidat att tidigt anta tekniken. AI inom mammografiscreening syftar till hälsofrämjande effekter genom en förhoppning om säkrare bilddiagnostik. Röntgensjuksköterskans (RSS) arbete präglas av korrekt utförd bildtagning och ett aktivt aktualiserande av den egna yrkesrollen gällande såväl tekniska framsteg som förnyade arbetssätt. Litteraturstudien har upprättats i syfte att belysa potentiella effekter av AI på bilddiagnostik inom mammografiscreening. Genom manifest innehållsanalys av resultat erhållna ur ämnesrelevanta vetenskapliga studier publicerade i databaserna Cinahl och Medline under år 2019–2020 identifierades och beskrevs kategorier sammanställda av subkategorier med liknande innehåll. Effekter inom granskningsprocessen och diagnostisk säkerhet skildrar flera perspektiv gällande AI:s effekter på bilddiagnostik. Utöver en stundtals ökad förmåga till cancerdetektion vid AI-assistans har artificiell bildgranskning även visat sig kunna reducera arbetsbördan för radiologer i form av friskrivning av mammogram med låg sannolikhet för bröstcancer. Vid tillämpning av AI ses lovande effekter inom framförallt klassificering av bröstvävnad samt vid reducering av falska positiva svar. Forskningen förbehålls dock med kvarstående etiska dilemman och avsaknad av ett juridiskt ramverk, vilket lämnar utrymme för vidare studier. / Recent developments in artificial intelligence (AI) and deep learning (DL) have made diagnostic imaging a prime candidate to adopt the technology. AI in mammography screening aims at promoting health with hopes of higher diagnostic accuracy. The radiographers work is characterized by properly performed imaging and actively updating the profession regarding technical developments and renewed working methods. The aim of this systematic review was to illustrate feasible effects of AI on diagnostic imaging within mammography screening. Through manifest content analysis of results obtained from subject related scientific studies published 2019–2020 in the databases Cinahl and Medline the authors identified and described categories compiled by subcategories with similar contents. Effects within the image interpretation process and diagnostic accuracy describes several perspectives regarding the outputs of AI on diagnostic imaging. AI-systems have proven to be useful in both assisting with image interpretation and reducing the workload for radiologists by disclaiming mammograms with low probability of breast cancer. Most promising effects are seen in the classification of breast tissue and reduction of false positives, but research is challenged by ethical dilemmas and the need for a legal framework, which are areas suggested for future research.

Background: Screening for breast and cervical cancers is important because early detection increases cancer survival. Since 1991, the U.S. government has helped finance a national early detection program for breast and cervical cancer among uninsured low-income women, known in Virginia (VA) as Every Woman's Life (EWL).Objectives: This study aims to determine whether there are differences in the prevalence of breast and cervical cancer screening based on insurance coverage, assess the prevalence of screening by health district, and evaluate the ability of EWL to effectively reach its target population.Methods: Cross-sectional data from the 2005 Behavioral Risk Factor Surveillance System survey were analyzed. Using population weights, descriptive statistics were generated and multiple regression was performed to assess the association between insurance coverage and screening prevalence among VA women 40 to 64 years of age (n=1,627). Adjusted screening prevalence by health district was also calculated. EWL client (n=4,959) and provider data from the VA Department of Health was obtained to create screening density maps with Geographic Information Systems.Results: After adjustment, women with insurance were significantly more likely to have a mammogram within the last two years compared to those without insurance (78% vs. 50%, pConclusion: The results of this study will be used to assist EWL in recruitment of additional screening and/or diagnostic sites in underserved areas of Virginia.

Segmentation of mammograms pertains to assigning a meaningful label to each pixel found in the image. The segmented mammogram facilitates both the function of Computer Aided Diagnosis Systems and the development of tools used by radiologists during examination. Over the years many approaches to this problem have been presented. A surge in the popularity of new methods to image processing involving deep neural networks present new possibilities in this domain, and this thesis evaluates mammogram segmentation as an application of a specialized neural network architecture, U-net. Results are produced on publicly available datasets mini-MIAS and CBIS-DDSM. Using these two datasets together with mammograms from Hologic and FUJI, instances of U-net are trained and evaluated within and across the different datasets. A total of 10 experiments are conducted using 4 different models. Averaged over classes Pectoral, Breast and Background the best Dice scores are: 0.987 for Hologic, 0.978 for FUJI, 0.967 for mini-MIAS and 0.971 for CBIS-DDSM.

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.