Thesis (PhD)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Breast cancer is a heterogeneous disease characterised by marked inter-individual variability in presentation, prognosis and clinical outcome. The recognition that morphological assessment has limited utility in stratifying patients into prognostic subgroups led to clinico-pathological classification of tumour biology, based on receptor expression using immunohistochemical (IHC) techniques. This standard is currently complemented by the development of gene expression profiling methodology that led to the identification of intrinsic molecular subtypes, reflecting tumour genetics as the true driver of biological activity in breast cancer. The study was based on the hypothesis that molecular classification of breast carcinomas integrated with established clinico-pathological risk factors will improve current diagnostic and risk management algorithms used in clinical decision-making. A pathology-supported genetic testing strategy was used to evaluate microarray-based gene profiling against diagnostic pathology techniques as the current standard. Clinico-pathological factors including age, number of positive axillary nodes, tumour size, grade, proliferation index and hormone receptor status was documented for 141 breast cancer patients (143 tumours) referred for microarray-based gene expression profiling between 2007 and 2014. Subsets of patients were selected from the database based on the inclusion criteria defined for three phases in which the study was performed, in order to determine 1) the percentage of patients stratified as having a low as opposed to high risk of distant recurrence using the 70-gene MammaPrint profile within the inclusion criteria, 2) correlation of HER2 status as determined by IHC and fluorescence in situ hybridisation (FISH) with microarray-based mRNA readout (TargetPrint), and 3) the relationship between hormone receptor determination as reported by standard IHC and molecular subtyping using the 80-gene BluePrint profile. Similar distribution patterns for MammaPrint low- and high-risk profiles were obtained irrespective of whether fresh tumour biopsies or formalin-fixed paraffin embedded (FFPE) tissue was used. During the first phase of the study, 60% of the 106 tumour specimens analysed with MammaPrint were classified as low-risk and 40% as high-risk using a newly-developed MammaPrint pre-screen algorithm (MPA) aimed at cost-saving. In the second phase of the study, performed in 102 breast tumours, discordant or equivocal HER2 results were found in four cases. Reflex testing confirmed the TargetPrint results in discordant cases, achieving 100% concordance regardless of whether fresh tumour or FFPE tissue was used for microarray analysis. For the third phase of the study 74 HER2-negative tumour samples were selected for comparative analysis. Statistically significant positive correlations were found between protein expression (IHC score) and mRNA (TargetPrint) levels for estrogen receptor (ER) (R=0.53, p<0.0001) as well as progesterone receptor (PR) (R=0.62, p<0.0001), while combined ER/PR tumour status was reported concordantly in 82.4% of these tumours. BluePrint was essential for interpretation of these results used in treatment decision-making. The MPA developed in South Africa in 2009 was validated in this study as an appropriate strategy to prevent chemotherapy overtreatment in patients with early-stage breast cancer. The use of microarray-based analysis proved to be a reliable ancillary method of assessing HER2 status in breast cancer patients. Risk reclassification based on the TargetPrint results helped to avoid unnecessary high treatment costs in false-positive cases, in addition to providing potentially life-saving treatment to those for whom it was indicated. While neither IHC nor TargetPrint estimation of intrinsic subtype correlated independently with the molecular subtype as indicated by BluePrint profiling, the ability to distinguish between basal-like and luminal tumours was enhanced when the combined protein and mRNA values was considered. Genomic profiling provided information over and above that obtained from routine clinico-pathological assessments. This finding supports the relevance of a pathology-supported genetic testing approach to breast cancer management, whereby advanced genomic testing is combined with existing clinico-pathological risk stratification methods for improved patient management. / AFRIKAANSE OPSOMMING: Borskanker is „n heterogene siekte wat gekenmerk word deur merkbare inter-individuele variasie in kliniese beeld, prognose en uitkoms. Die beperkings van morfologiese klassifikasie vir identifikasie van prognostiese subgroepe het gelei tot klinies-patologiese tumor karakterisering op grond van reseptor uitdrukking deur gebruik van immunohistochemiese (IHC) toetse. Hierdie standaard word tans gekomplementeer deur ontwikkeling van geenuitdrukking tegnologie wat gelei het tot die identifikasie van intrinsieke molekulêre subtipes, wat die tumor genetika reflekteer as die ware drywer van biologiese aktiwiteit in borskanker. Die huidige studie is gebaseer op die hipotese dat integrasie van die molekulêre klassifikasie van borskanker met konvensionele risiko klassifikasie skemas huidige diagnostiese en behandelings algoritmes kan verbeter vir kliniese besluitneming. „n Patologie-gesteunde strategie is gebruik om mikroplaat-gebaseerde geen profilering te evalueer teen standaard patologie diagnotiese tegnieke. Kliniese-patologiese faktore insluitend ouderdom, aantal positiewe aksillêre limfnodes, tumor grootte, gradering, proliferasie indeks en hormoon reseptor status is gedokumenteer in 141 borskanker pasiente (143 tumore) wat verwys is vir mikroplaat-gebaseerde geenuitdrukking profilering tussen 2007 en 2014. Pasiënt subgroepe is geselekteer uit die databasis volgens die insluitingskriteria soos gedefiniëer in die drie fases waarvolgens hierdie studie uitgevoer is, om vas te stel 1) watter proporsie pasiënte geklassifiseer word as lae- of hoë-risiko vir latere herhaling van die borskanker deur gebruik van die 70-geen MammaPrint profile binne die insluitingskriteria, 2) hoe korreleer HER2 status soos vasgestel deur IHC en fluoreserende in situ hybridisasie (FISH) toetsing met mikroplaat-gebaseerde RNA lesings (TargetPrint), en 3) wat die verwantskap is tussen hormoon reseptor status soos deur standaard IHC gerapporteer en molekulëre klassifikasie volgens die 80-geen BluePrint profiel. Soortgelyke verdelingspatrone vir MammaPrint lae- teenoor hoe-risiko profiele is waargeneem ongeag of vars tumor biopsies of formalien-gefikseerde paraffin bevattende weefsel gebruik is. Tydens die eerste fase van die studie is 60% van die 106 tumore as lae-risiko en 40% as hoë-risiko geklassifiseer met toepassing van die nuwe MammaPrint Presifting Algoritme (MPA) wat ontwikkel is met die doel op kostebesparing. In die tweede fase van die studie waar 102 tumore ingesluit is, het die resultate van vier gevalle verskil van mekaar of was onbepaald ten opsigte van HER2 status. Refleks herevaluering het die TargetPrint resultate bevestig in alle nie-ooreenstemmende gevalle, en 100% ooreenstemming is bereik ongeag of vars tumor biopsies of formalien-gefikseerde paraffin bevattende weefsel gebruik is vir mikroplaat analise. In die derde fase van die studie is 74 HER2-negative tumore selekteer vir vergelykende analise. Statisties beduidende positiewe korrelasies is waargeneem tussen proteïen uitdrukking (IHC) en mRNA (TargetPrint) vlakke vir die estrogeen reseptor (ER) (R=0.53, p<0.0001) sowel as progesteroon reseptor (PR) (R=0.62, p<0.0001), terwyl gekombineerde ER/PR reseptor status ooreenstemming getoon het in 82.4% tumore. BluePrint was noodsaaklik vir die korrekte interpretasie van die resultate wat gebruik is in kliniese besluitneming vir behandeling van pasiënte. The MPA wat in Suid Africa ontwikkel is in 2009, is gedurende hierdie studie bevestig as n toepaslike strategie om onnodige handeling met chemoterapie te voorkom in pasiënte met vroeë stadium borskanker. Die gebruik van mikroplaat-gebaseerde analise is aangetoon as „n betroubare aanvullende metode om HER2 status te evalueer. Risiko herklassifikasie gebaseer op TargetPrint resultate het onnodige hoë behandelingskoste in vals-positiewe gevalle vermy, sowel as om die verskaffing van potensieël lewensreddende behandeling vir die toepaslike pasiënte te verseker. Genomiese profilering het inligting addisioneel tot dit wat met roetine klinies-patologies metodes verkry kan word verskaf. Hierdie bevinding ondersteun die relevansie van „n patologie-gesteunde genetiese toets benadering tot hantering van borskanker, waardeur genomiese toetsing gekombineer word met bestaande klinies-patologiese risiko stratifisering metodes om pasiënt behandeling te verbeter.

Diagnoses using images made with non-ionizing ultrasound are based on qualitive criteria and are not more accurate than those made with mammography. Information about tissue state is lost in the processing required to produce ultrasound images, and textural information may not be perceptible to a human observer. This study uses statistical pattern recognition to classify ultrasound A-scans, before any processing other than amplification occurs. A U. I. Octoson was used to collect data from normal, benign, and malignant, in vivo breast tissues. Features based on textural or frequency content of received sound were computed from digitized A-scans. Most textural features have been used previously in image processing, while frequency features assumed differences in frequency-dependent attenuation. Data were collected at the University of Arizona from 17 malignant masses, 8 benign masses, and 7 normal tissues. Univariate and multivariate statistical tests were used to find combinations of features which discriminated best between the classes of tissue. Equal a priori probabilities were used in a Bayesian classifier to classify malignant vs. nonmalignant. Specificity of 76% (13 of 17 malignant masses correct) was found with a sensitivity of 80% (12 of 15 masses correct). A linear combination of one frequency feature and three textural features was used. For malignant vs. benign, sensitivity of 88% (15 of 17 masses) and specificity of 75% (6 of 8 masses) were found. Features used were the same as for classification of malignant vs. nonmalignant, except for modification of one textural feature. The inability to visually detect and gather data from some palpable masses means that further study is needed to determine the effectiveness of applying the method to all breast masses. A set of A-scans from Thomas Jefferson Hospital in Philadelphia was gathered using similar procedures, and analysed with the following results: 18 of 21 (86%) malignant masses, and 45 of 66 (68%) nonmalignant masses were classified correctly, using a linear combination of one textural feature and five frequency features. Confidence limits on the results show that the majority of masses can be classified correctly with this procedure, but success rates are not high enough for breast cancer screening.

There is considerable evidence that delays in diagnosing and treating breast cancer reduce long-term survival. The purpose of this study was to assess the waiting time between diagnosis and treatment for Alberta women with breast cancer and to examine the influence of age, cancer stage, Regional Health Authority (RHA), community size, and year of diagnosis on this time interval. The data were obtained from the Alberta Cancer Board. The information included approximately all Alberta women with breast cancer between 1997 and 2000. The overall median waiting time was 17 days. The mean and median delay increased by an average of two days each year. Only 43.8% of cases were treated within the recommended 14 days. The delay was significantly longer for women younger than 70, with stage 1 disease and from Northern RHAs. Efforts must be made to decrease delay and ensure that all women receive equal access to health services. / xii, 106 leaves : ill. ; 28 cm.

This research is a follow up of a "Breast Week" which was organized in Freetown, Sierra Leone. The specific objective of this study was to assess the effectiveness of the knowledge and teachings given to the women who participated in this project. The unrecorded cases of breast lumps and breast cancer observed in women in Sierra Leone prompted the researcher to undertake this present study. A quantitative approach was adopted and a structured interview schedule and an observational checklist guided the data collection process. A sample size of 120 women (10%) who participated in the "breast week" was obtained through systematic sampling. The first part of the study involved assessment of the theoretical background of the research topic followed by the second phase during which the women demonstrated Breast Self-Examination to detect abnormalities of the breasts. Discussions and analysis of the findings are presented in three sections. Texts from open ended questions were categorized and explained in numerical terms as the study was quantitative in nature. The data was processed through use of SPSS and Microsoft Excel. Frequency counts were applied to the data, use of non-parametric tests on the number of women who practiced Breast Self-Examination before and after the breast week showed a statistically significant difference in the number of women now practicing BSE as a screening method for breast cancer after receiving the health education. It was found that the majority of the women linked breast cancer to the signs and symptoms associated with it and were able to describe the disease as one that kills women if not promptly detected and/or treated appropriately. Findings indicate that the majority of the women (78.3%) had previously had mmor breast problems. An assessment of the effectiveness of knowledge on breast cancer showed that these women could identify breast cancer as a disease that affects women and may cause deaths if not detected on time or treated promptly. These women were able to demonstrate to the researcher how they examine their breasts to exclude abnormalities. Three women had breast lumps detected through examination of the breasts during the breast week. Two of them had had the lumps removed and are currently on medication. One of the women who had a breast lump detected was financially constrained and could not afford the cost of surgery. The number of women who can now perform BSE increased (95.0%) after having the knowledge on breast cancer and BSE. The majority of the women (97.4%) received information on how to examine their breasts for breast cancer through the information provided during the breast week. It is thus concluded that the objectives of the breast week were met. / Thesis (M.N.)-University of Natal, Durban, 2004.

Magnetic Resonance Elastography (MRE) is an emerging method for non-invasive breast cancer screening. It takes the MRI displacement data output and reconstructs the internal stiffness distribution, where cancerous tissue is approximately five to ten times stiffer than healthy breast tissue. Hence, MRE offers a high contrast solution to this diagnostic problem. Current MRE methods for reconstructing stiffness use forward simulation based optimization methods that are highly non-linear, non-convex and very heavy computationally. This research develops integral-based inverse problem solutions that reformulate the underlying differential equations in terms of integrals of MRI measured displacement data, and this transforms the problem into a linear, convex optimization. All derivative terms in the formulation are removed by special choice of integration limits, so no smoothing or filtering of the input data is required. The resulting equations can easily be solved by linear least squares requiring very minimal computation. 1D inverse algorithms were developed to provide a proof of concept of the integral-based method. Initially, the complete compressible 2D Navier's equations were used to develop the 2D inverse methods. Reasonable results were achieved with the algorithm successfully identifying a 1cm by 1cm tumour with up to 10% noise, data resolution of 20 measured points per cm and actuation frequencies of 100Hz. However, for the same input data set, a simplified incompressible 2D model was used as the basis for the final proposed inverse algorithm. This approach significantly improved results by removing ill-conditioned terms from the original formulation. For a 1cm by 1 cm tumour, accurate results were obtained with up to 40% noise, a range of actuation frequencies and very low data resolution of the order of 2 measured points per cm. These results thus indicate that more crude and less expensive data measurement systems could be used to obtain good results. The methods developed can be readily extended to 3D by applying a similar incompressible integral formulation to the 3D Navier's equations.

This thesis focuses on the modelling and prediction of factors affecting attendance to screening invitations of the NHS Breast Screening Programme. The analysis is based on data collected by the Warwickshire, Solihull and Coventry Breast Screening Unit from 1989 up to 2001 with respect to invitation to screening for the prevention of breast cancer in non-symptomatic women. Using a novel approach to the analysis of the data, from the perspective of the screening episode of each woman, rather than the usual analysis from the perspective of the screening round of the units, a statistical analysis is carried out on the whole registered population for the first time. Amendments to the current formulae for coverage calculations, the introduction of a new parameter (invitation rate) and the proposal for a reduction of the invitation period (period of time between two consecutive invitations) follows from the analysis. A preliminary analysis of predictive methodologies, including traditional statistical methods and artificial intelligent methods, gives the foundation to the formulation of two new algorithms; the first, for the prediction of attendance of women to screening invitations, and the second for the prediction of occurrence of screening variation (change of appointment dates) of women to invitations. Both algorithms are based on neural network generated models able to learn from the previous screening behaviour history of the woman, a technique not previously explored for the prediction of attendance. The accuracy of the new proposed algorithm for the prediction of attendance to invitation is tested on a blind study using data not previously seen by the predictive system, and for which results were unknown at the time when the predictions were made. From the obtained results, it is concluded to recommend the implementation by the NHS Breast Screening Unit of the two algorithms proposed for the prediction of the women’s attendance and screening variation to their invitation for screening. With these predictions, women likely not to attend, or change appointment date, can be identified and appropriately targeted with the aim of increasing their attendance in the short term, and in the long term, reducing breast cancer mortality.

Includes bibliographical references (18 leaves). / 2 v. : ill., maps ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Examines the factors that predict attendence to mammography screening by comparing various groups of attenders and non-attenders. / Thesis (Ph.D.)--University of Adelaide, Dept. of Public Health, 1999

Thesis (MTech (Nursing))--Cape Peninsula University of Technology, 2015. / The most common cancer in women worldwide is breast cancer. It is also the leading cancer affecting women in South Africa. When breast cancer is detected early, it improves the outcome of the disease and reduces mortality. The aim of this study was to determine the knowledge, beliefs, attitude and practice of breast self-examination among female university students. The objectives were, to explore the levels of knowledge of female university students on breast cancer and breast self-examination; to ascertain the beliefs of female university students on breast cancer and breast self-examination; to examine the attitudes of female university students toward breast cancer and breast self-examination and to determine if female university students regularly practice breast self-examination. A Mixed method descriptive design was used for this study. The selected site for this study was a higher education institution in the Western Cape. The population included all female university students in the Western Cape. The sample was female university students studying in the selected higher education institution who reside on the institution’s campus. Convenience sampling was used to select the sample. Two methods were used to collect data; these were questionnaires and face-to-face interviews. Questionnaires were analysed by the use of Microsoft Excel and Statistical Package for Social Sciences. Frequency Distribution was used to analyse descriptive statistics. Interviews were transcribed and analysed by using coding and thematic analysis. Participants lacked knowledge on breast cancer risk factors, as majority of them only knew about family history being a risk factor. Majority of the participants had never been educated by their healthcare provider on breast cancer and its screening. Most of the participants had never examined their breast before. Most of the participants who did not examine their breast did not have any knowledge on how to do BSE. Education on breast cancer and cancer as a whole should be initiated in high schools and higher institutions of learning as part of their curriculum. Posters on breast cancer screening and breast self-examination should be put up at public places and campuses. Breast awareness campaigns must be done every month not only in October which is the breast cancer awareness month. Health care professionals should give information on breast cancer to women when they visit the hospital or health centre

Thesis (MSc (Radiography))--Cape Peninsula University of Technology, 2019 / INTRODUCTION: The Breast Imaging Reporting and Data System (BI-RADS) was developed by the American College of Radiology (ACR). The BI-RADS is an internationally accepted method of assessing and reporting on mammograms and breast ultrasound images. The BI-RADS consists of a lexicon (descriptors) and assessment categories. The ACR aimed to standardise mammography reporting and placing the findings in the appropriate assessment category. The aim of this study was to establish the accuracy of the BI-RADS assessment categories for mammography and breast ultrasound images in women diagnosed with breast cancer. METHOD: Data were retrieved from 77 patients who were diagnosed with breast cancer from 1 January 2013 to 31 December 2014. Seven did not meet the inclusion criteria and were excluded. The study sample size was 70 (n=70) patients. All mammography reports included a BI-RADS assessment category of all patients diagnosed with breast cancer within the study period. These reports were analysed and compared with histopathology results. The BI-RADS assessment category and descriptors were collected from the mammogram reports; the histopathology report indicated the type of breast cancer. All reports were obtained from the patients' folders at the research site. In addition, questionnaires were distributed among radiologists to assess whether their experience and training had an influence on the accuracy of reporting in the BI-RADS assessment categories. Descriptive and inferential statistical analysis was used for data analysis. RESULTS: The most common malignancy diagnosed was invasive ductal carcinoma with a total of 70% (n=54), followed by ductal carcinoma in situ with 10.4% (n=8) and invasive lobular carcinoma with 9.1% (n=7). The histology results confirmed breast cancer for all BI-RADS 4 and 5 assessment categories. The mammogram was able to detect 93.5% of abnormalities and breast ultrasound 84.4% of abnormalities in this study sample. Breast ultrasound was used as an adjunct to mammography and hence an overall combined diagnostic rate was 100%. Mammography descriptors: The more common malignancy findings were spiculated mass margin, 35.1% (n=27). Ultrasound descriptors: The more common malignancy findings were hypoechoic echo pattern, 55.8% (n=43). There was no significant association (p=0.152) between the radiologists' years of experience and BI-RADS 3, 4 and 5 assessment category reporting. Of the 15 responses, 67% agreed that the BI-RADS standardises breast imaging reporting and reduces confusion, 33% agreed that the BI-RADS allows better communication between radiologists and referring physicians, and 40% agreed that the BI-RADS clarifies further management for patients by helping to stratify risk management. CONCLUSION: The outcome of this study indicated that the use of BI-RADS assessment categories is useful for predicting the likelihood of malignancy when used correctly. The outcome of BI-RADS 4 and BI-RADS 5 had a positive predictive value of 100%, which corresponded well with histology results. The descriptor findings suggested that spiculated mass margins, irregular-shaped masses, hypoechoic echo pattern and posterior shadowing were high predictors of malignancy and warranted a placement in the BI-RADS 5 assessment category.

Solid tumors can hijack many of the same programs used in neurogenesis to enhance tumor growth and metastasis, thereby generating a plethora of neurogenesis-related molecules including semaphorins Among them, we have identified Semaphorin7A (SEMA7A) in breast cancer We first used to the DA-3 mammary tumor model to determine the effect of tumor-derived SEMA7A on immune cells We found that tumor-derived SEMA7A can modulate the production of proangiogenic chemokines CXCL2/MIP-2 and CXCL 1, and prometastatic MMP-9 in macrophages We next aimed to determine the expression and function of SEMA7A in mammary tumor cells We found that SEMA7A is highly expressed in both metastatic human and murine breast cancer cells We show that both TGF-β and hypoxia elicits the production of SEMA 7 A in mammary cells SEMA7 A shRNA silencing in 4T1 cells resulted in decreased mesenchymal markers MMP-3, MMP-13, Vimentin and TGF-β) SEMA7A silenced cells show increased stiffness with reduced migratory and proliferative potential In vivo, SEMA7A silenced 4T1 tumor bearing mice showed decreased tumor growth and metastasis Genetic ablation of host-derived SEMA7A synergized to further decrease the growth and metastasis of 4T1 cells Our findings suggest novel functional roles for SEMA7A in breast cancer and that SEMA7A could be a novel therapeutic target to limit tumor growth and metastasis / Includes bibliography / Dissertation (PhD)--Florida Atlantic University, 2016 / FAU Electronic Theses and Dissertations Collection