published_or_final_version / Public Health / Master / Master of Public Health

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Physics, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography: leaves 190-193. / by Bruce Robert Rosen. / M.S.

Breast cancer (BC) survivors are at increased risk of cancer recurrence, a second cancer, and non-cancer comorbidities. Previous studies suggest that many women adopt a spontaneous change in lifestyle after a BC diagnosis in hope of achieving a better survival outcome. While this observation has led to the suggestion that a BC diagnosis is a “teachable moment” for improving health behaviors, other conflicting studies report that BC survivors do not make positive changes in health behaviors following a breast cancer diagnosis. Although previous studies suggest that receipt of cancer chemotherapy and hormonal therapy is associated with weight loss or weight gain, the association between post-diagnosis weight change with changes in lifestyle has not been studied in detail. The majority of prior studies of post-diagnosis changes in behavior and weight have examined the mean change between two time points, and therefore may over simplify the trajectory of change over time due to lack of more granular data. New methods are needed to examine the distribution and correlates of behavior/weight trajectories following the BC diagnosis. In my dissertation, a systematic literature review was conducted to evaluate the evidence regarding the frequency, magnitude and pattern of post-diagnosis changes in diet [fruit/vegetable (F/V), dietary fat], physical activity [moderate to vigorous physical activity (MVPA) and sedentary behaviors], alcohol intake, and body weight among BC survivors. A total of 66 studies were included in the systematic review. These studies suggest that after a breast cancer diagnosis, women are less likely to engage in MVPA and more likely to reduce alcohol intake. Previous studies suggested that women may experience weight change after a BC diagnosis, although there were strong evidence showing both weight gain and weight loss were common. The reports of changes in diet and sedentary behavior following a BC diagnosis are limited and inconclusive about the direction of change. The results of the review suggested that there is wide variation in post-diagnosis lifestyle changes among BC survivors. However, very few studies have investigated the variability in multiple behavior trajectories following a BC diagnosis. In this dissertation, I made use of a population of 4,505 women newly diagnosed with a BC and enrolled in the Kaiser Permanente Northern California Pathways Study. I used a combination of statistical methods, including a semi-parametric, group-based trajectory modeling and a non-parametric K-means for longitudinal data analysis, to identify latent trajectories groups that are unobserved clusters of individuals following similar trajectories of a behavior. These analyses tested the hypotheses that in the 24 months following a breast cancer diagnosis, women follow a mixture of lifestyle (F/V, dietary fat, MVPA, sedentary behavior, alcohol) and body mass index (BMI) trajectories, which can be stable, temporarily increase or temporarily decrease. My analysis identified multiple distinct trajectories of lifestyle behaviors and BMI during the first 24 months after a BC diagnosis. The trajectory analysis results suggest that the large majority of women maintained their lifestyles following a BC diagnosis. Socioeconomic status, dispositional optimism, perceived social support, and the severity of CIPN during active treatment were associated with the post-diagnosis trajectories of. Furthermore, the BMI trajectories were stable over the first 24 months following a BC diagnosis. The BMI trajectories were associated with trajectories of F/V, dietary fat intake, MVPA, sedentary behavior and alcohol intake over the same period, independent of demographic characteristics, tumor characteristics and cancer treatment received. In summary, previous studies suggest that women may spent fewer time on MVPA and drink less alcohol after a BC diagnosis, while both weight gain and loss are common post diagnosis. In a trajectory analysis of 4505 BC survivors enrolled in the Pathways Study, I did not observe any latent trajectory of meaningful change in health behavior or BMI in the first 24 months after a BC diagnosis in the Pathways Study. Instead, my analysis suggests that most women maintained their body weight following a BC diagnosis. The BMI trajectories were strongly associated with trajectory of F/V, dietary fat intake, MVPA, sedentary behavior, and alcohol intake over the same period, independent of demographic characteristics, tumor characteristics and receipt of cancer therapies. These results suggest that there is an absence of spontaneous changes in lifestyle behaviors after BC diagnosis and the importance of maintaining a healthy lifestyle in weight management after a BC diagnosis. Future studies should examine the associations of these health behaviors and BMI trajectories and BC prognosis to better understand the effect of post-diagnosis changes in lifestyle and weight on BC-specific and all-cause mortality.

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The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.

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This thesis proposes four novel geometric neurodynamical classifier models, namely GBAM, Lie-derivative, Lie-Poisson, and FAM, applied to breast cancer detection. All these models have been published in a paper and/or in a book form. All theoretical material of this thesis (Chapter 2) has been published in my monographs (see my publication list), as follows: 2.1 Tensorial Neurodynamics has been published in Natural Biodynamics (Chapters 3, 5 and 7), Geometrical Dynamics of Complex Systems; (Chapter 1 and Appendix), 2006) as well as Applied Differential Geometry:A Modern Introduction(Chapter 3) 2.2 GBAM Neurodynamical Classifier has been published in Natural Biodynamics (Chapter 7) and Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapter 3), as well as in the KES–Conference paper with the same title; 2.3 Lie-Derivative Neurodynamical Classifier has been published in Geometrical Dynamics of Complex Systems; (Chapter 1) and Applied Differential Geometry: A Modern Introduction (Chapter 3); 2.4 Lie-Poisson Neurodynamical Classifier has been published in Geometrical Dynamics of Complex Systems; (Chapter 1) and Applied Differential Geometry: A Modern Introduction (Chapter 3); 2.5 Fuzzy Associative Dynamical Classifier has been published in Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapter 4), as well as in the KES-Conference paper with the same title. Besides, Section 1.2 Artificial Neural Networks has been published in Natural Biodynamics (Chapter 7) and Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapter 3). Also, Sections 4.1. and 4.5. have partially been published in Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapters 3 and 4, respectively) and in the corresponding KES–Conference papers. A. The GBAM (generalized bidirectional associative memory) classifier is a neurodynamical, tensor-invariant classifier based on Riemannian geometry. The GBAM is a tensor-field system resembling a two-phase biological neural oscillator in which an excitatory neural field excites an inhibitory neural field, which reciprocally inhibits the excitatory one. This is a new generalization of Kosko’s BAM neural network, with a new biological (oscillatory, i.e., excitatory/inhibitory)interpretation. The model includes two nonlinearly-coupled (yet non-chaotic and Lyapunov stable) subsystems, activation dynamics and self-organized learning dynamics, including a symmetric synaptic 2-dimensional tensor-field, updated by differential Hebbian associative learning innovations. Biologically, the GBAM describes interacting excitatory and inhibitory populations of neurons found in the cerebellum, olfactory cortex, and neocortex, all representing the basic mechanisms for the generation of oscillating (EEG-monitored) activity in the brain. B. Lie-derivative neurodynamical classifier is an associative-memory, tensor-invariant neuro-classifier, based on the Lie-derivative operator from geometry of smooth manifolds. C. Lie-Poisson neurodynamical classifier is an associative-memory, tensor-invariant neuro-classifier based on the Lie-Poisson bracket from the generalized symplectic geometry. D. The FAM-matrix (fuzzy associative memory) dynamical classifier is a fuzzy-logic classifier based on a FAM-matrix (fuzzy phase-plane). All models are formulated and simulated in Mathematica computer algebra system. All models are applied to breast cancer detection, using the database from the University of Wisconsin and Mammography database. Classification results outperformed those obtained with standard MLP trained with backpropagation algorithm. / Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2008

A quasi-experimental design was used in this study to determine if guided practice would result in higher breast cancer knowledge scores, higher breast self-examination (BSE) knowledge scores and higher intent to practice scores then modeling alone or teaching without modeling or guided practice. Fifty-eight women participated in the study. There were 19 women in the guided practice group, 22 in the modeling group and 17 in the control group. A pre-test, post-test format was used. ANCOVA was used to statistically control for the variance in pre-test scores. Descriptive statistics were used to analyze demographic data. The results showed that all methods of teaching resulted in higher breast cancer and BSE knowledge scores and in higher intent to practice scores. There were no statistically significant differences between the groups. Nursing research need to continue in this area to find the most effective method of teaching women breast self-examination.

In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.

Breast cancer is the most common cancer in women, accounting for almost one-thirdof new cancer diagnoses in the United States. The mortality rate has decreased by 42% since 1989 due to early diagnosis, improvements in imaging techniques and treatment regimens. Despite all the advances in imaging modalities, there is still a need for a non-invasive, nonionizing, and low-cost diagnosis technique with high sensitivity and specificity to reduce the rate of invasive biopsies. For individuals diagnosed with locally advanced breast cancer and early-stage breast cancer, neoadjuvant chemotherapy (NACT) has become the standard of care. Pathologic complete response (pCR) is the ideal outcome of NACT, which is correlated with the prognosis and overall survival of the patients. The pCR is achieved in only about 15-20% of patients determined at the time of surgery; therefore, most patients receive a treatment that is not beneficial for them and has considerable side effects. Thus, early detection and monitoring of breast tumor response to NACT is critical for treatment planning and improving overall survival. Ultrasound-based elasticity imaging techniques have gained interest in the clinic due to their potential to provide qualitative and/or quantitative information about tissue stiffness, which is presently not unachievable with standard ultrasonography. These techniques rely on the fact that a breast tumor’s stiffness or Young’s modulus is higher than that of the surrounding normal tissues. In this dissertation, the clinical feasibility of a technique called harmonic motion imaging (HMI) for breast tumor classification, as well as for NACT response prediction and monitoring of solid tumors is investigated. HMI is an ultrasound-based elasticity imaging technique that evaluates the mechanical properties of the underlying tissues by inducing amplitude modulated (AM) displacements at a specific frequency. First, we investigated whether HMI can characterize and differentiate human breast tumors based on their relative stiffness. We enrolled female patients with benign and malignant tumors and imaged them with a clinical HMI system. The malignant tumors were found to be associated with lower HMI displacements or higher stiffness than the benign tumors. Then, in order to verify our clinical findings, we estimated HMI displacements in the postsurgical breast specimens from the same subjects and compared them against the in-vivo estimations. Our findings indicated that HMI successfully differentiated tumors from the surrounding tissue in both ex-vivo and in-vivo conditions, with an excellent correlation between the results in the two different settings. Second, we introduced and characterized a new HMI setup consisted of a multi-element focused ultrasound transducer (FUS) with electronic beam steering capability. Therefore, instead of mechanical translation of the HMI setup, the acoustic force could be electronically steered in the volumetric space to accelerate the data acquisition. A pulse sequence was developed to drive the HMI transducers assembly, the FUS and imaging transducer, using a single ultrasound data acquisition system to have a compact setup that is more applicable for clinical settings. The data acquisition was further improved by investigating the effect of AM frequencies on the quality of the HMI images and tumor detection. We found that higher AM frequencies are needed in order to improve the detection and characterization of small and stiff inclusions. On the contrary, soft and large inclusions are better resolved at lower AM frequencies. Lastly, we investigated the feasibility of using HMI for early prediction of response to neoadjuvant chemotherapy in cancer mouse models and breast cancer patients. We acquired longitudinal HMI images from pancreatic and breast cancer murine tumors during treatment with chemotherapeutic drugs and monitored the changes in the mechanical properties of the tumors. The tumors were found to soften when responsive to treatment, followed by the stiffness increase in the case of drug resistance. However, the untreated mice underwent steady stiffening of the tumors. Next, we imaged breast cancer patients at different timepoints during their chemotherapy treatment. We found that tumors in the patients who achieved pCR had higher pre-treatment stiffness and higher softening from pre-treatment to a short-interval follow-up on treatment compared to the ones in patients with residual cancer cells at the completion of treatment. These findings indicate the promising potential of HMI in the early prediction of solid tumor response to chemotherapy interventions.