In breast cancer assessment, tissue removed during biopsy or surgery is sent to a pathology lab for analysis. To achieve high sensitivity for detecting disease, the diagnostic gold standard requires submission of a substantial portion of the resected specimen, which results in a labor and time-intensive process to obtain a diagnosis. There is an unmet need to identify regions of diagnostic interest in breast tissue samples to increase the efficiency of the clinical pathology workflow. Optical coherence tomography (OCT), a noninvasive imaging modality capable of depth-resolved, high-resolution, and in vivo imaging of tissue at large fields of view, enables effective assessment of this tissue. However, there is a two-fold problem: the large size of resected tissue to be imaged within clinical time constraints, and the high density of multi-dimensional OCT image data. An approach that enables comprehensive imaging by reducing both imaging time and data density is compressed sensing (CS), a theory that enables undersampling far below the Nyquist sampling rate and guarantees high accuracy image recovery. Therefore, the objective of this work is to demonstrate that compressed sensing techniques can be applied to OCT imaging to revise current optical hardware and improve the efficiency of image acquisition. CS-OCT has high potential for significantly altering the presently established workflow for breast cancer assessment. In this work, we prove that current OCT systems require further reduction of data sampling rate, to enable effective integration of the systems into the clinical pathology workflow. In addition, we identify challenges associated with the matching of OCT and histologic data that may be important to consider in the context of in vivo imaging. Further, we demonstrate the application of a novel and improved compressed sensing algorithm capable of reconstructing OCT volumes from highly undersampled imaging data. We show that these reconstructions preserve high spatial resolution and key image features, and we illustrate its improved performance over traditional reconstruction methods. Lastly, we integrate our compressed sensing techniques to physical OCT hardware. We demonstrate a pilot OCT system that integrates efficient undersampling schemes with subsequently successful 3-D image reconstructions. We evaluate acquisition patterns that take advantage of the typical forward and backward scan cycle of OCT systems to accomplish native subsampling of target data to varying degrees of compression. Using our CS-OCT algorithm, we successfully reconstruct OCT image volumes and demonstrate qualitative and quantitative preservation of image quality down to compression levels of 5% of total data.

Chronic obstructive pulmonary disease (COPD) and emphysema together are one of the leading causes of death in the United States and worldwide; meanwhile, breast cancer has the highest incidence and second-highest mortality burden of all cancers in women. Imaging markers relevant to each of these conditions are readily identifiable on chest computed tomography (CT): (1) visually-appreciable variants in airway tree structure exist which are associated with increased odds for development of COPD; (2) CT emphysema subtypes (CTES), based on lung texture and spatial features, have been identified by unsupervised clustering and correlate with functional measures and clinical outcomes; (3) dysanapsis, or the ratio of airway caliber to lung volume, is the strongest known predictor of COPD risk, and (4) breast density (i.e., the extent of fibroglandular tissue within the breast) is strongly associated with breast cancer risk. Machine- and deep-learning frameworks present an opportunity to address unmet needs in each of these directions, leveraging the data from large CT cohorts. Application of unsupervised learning approaches serves to discover new, image-based phenotypes. While topologic and geometric variation in the structure of the CT-resolved airway tree are well-described, tree- structural subtypes are not fully characterized. Similarly, while the clinical correlates of CTES have been described in large cohort studies, the association of CTES with structural and functional measures of the lung parenchyma are only partially described, and the time-dependent evolution of emphysematous lung texture has not been studied. Supervised approaches are required to automate CT image assessment, or to estimate CT- based measures from incomplete input data. While dysanapsis can be directly quantified on full- lung CT, the lungs are often only partially imaged in large CT datasets; total lung volume must then be regressed from the observed partial image. Breast density grades, meanwhile, are generally visually assessed, which is laborious to perform at scale. Moreover, current automated methods rely on segmentation followed by intensity thresholding, excluding higher-order features which may contribute to the radiologist assessment. In this thesis, we present a series of machine-learning methods which address each of these gaps in the field, using CT scans from the Multi-Ethnic Study of Atherosclerosis (MESA), the SubPopulations and InteRmediate Outcome Measures in COPD (SPIROMICS) Study, and an institutional chest CT dataset acquired at Columbia University Irving Medical Center. First, we design a novel graph-based clustering framework for identifying tree-structure subtypes in Billera-Holmes-Vogtmann (BHV) tree-space, using the airway trees segmented from the full-lung CT scans of MESA Lung Exam 5. We characterize the behavior of our clustering algorithm on a synthetic dataset, describe the geometric and topological variation across tree-structure clusters, and demonstrate the algorithm’s robustness to perturbation of the input dataset and graph tuning parameter. Second, in MESA Lung Exam 5 CT scans, we quantify the loss of small-diameter airway and pulmonary vessel branches within CTES-labeled lung tissue, demonstrating that depletion of these structures is concentrated within CTES regions, and that the magnitude of this effect is CTES-specific. In a sample of 278 SPIROMICS Visit 1 participants, we find that CTES demonstrate distinct patterns of gas trapping and functional small airways disease (fSAD) on expiratory CT imaging. In the CT scans of SPIROMICS participants imaged at Visit 1 and Visit 5, we update the CTES clustering pipeline to identify longitudinal emphysema patterns (LEPs), which refine CTES by defining subphenotypes informative of time-dependent texture change. Third, we develop a multi-view convolutional neural network (CNN) model to estimate total lung volume (TLV) from cardiac CT scans and lung masks in MESA Lung Exam 5. We demonstrate that our model outperforms regression on imaged lung volume, and is robust to same- day repeated imaging and longitudinal follow-up within MESA. Our model is directly applicable to multiple large-scale cohorts containing cardiac CT and totaling over ten thousand participants. Finally, we design a 3-D CNN model for end-to-end automated breast density assessment on chest CT, trained and evaluated on an institutional chest CT dataset of patients imaged at Columbia University Irving Medical Center. We incorporate ordinal regression frameworks for density grade prediction which outperform binary or multi-class classification objectives, and we demonstrate that model performance on identifying high breast density is comparable to the inter-rater reliability of expert radiologists on this task.

Shared characteristics and predictors of psychological distress are beginning to be identified in research on women seeking genetic testing for BRCA1 and BRCA2 gene mutations. This study further explored patterns of psychological distress for 51 community women waiting to receive such genetic test results. There was no significant relationship between psychological distress and family cancer history, personal cancer history, social support networks, and family environment. Women in this sample tended to rely more on females and relatives for support than males and friends. Social support satisfaction was not related to gender or number of relatives providing support. Thirty-four of the 36 women classified on the family environment type were from Personal Growth-Oriented families. Comparisons with normal and distressed family means revealed increased cohesion and expressiveness with decreased conflict, indicative of supportive family environments. Limitations and implications are discussed.

[Truncated abstract] Background: A review of the literature indicated there was a need for more long-term randomised controlled studies on the effects of BRCA counselling/testing on high risk women, including improved strategies for risk communication. Reviews have also shown women are confused about the significance of inconclusive or non informative results with a need for more research in this area. Aims: The general aim of this study was to evaluate the impact of breast cancer genetic counselling on psychological distress levels, perception of risk, genetic knowledge and understanding of BRCA testing/test results in a cohort of 207 women from high risk breast cancer families who were referred for genetic counselling in Perth during the period 1997 to 2001. Short- and long-term impact of BRCA genetic counselling/testing was determined in women with and without cancer in a randomised controlled trial as part of which women were randomised to either receive immediate versus delayed genetic counselling. This included family communication patterns before BRCA testing, anticipated outcomes of testing on oneself and family including intentions for result disclosure. Comprehension of index and predictive BRCA testing with possible results was assessed both in the short- and the long-term and understanding of individual or family BRCA test results was evaluated at long-term. The effect of genetic counselling on breast cancer risk perception in unaffected women was evaluated. This study considered a theoretical framework of educational learning theories to provide a basis for risk communication with possible relevance for future research. ... Only 25% of the original study population (52/207) reported BRCA results and women's understanding of results is concerning. Key findings were: 1. The majority of affected women received an inconclusive result. 2. Out of twelve unaffected women who reported results, seven were inconclusive which are not congruent with predictive testing. This implies that these women did not understand their test result. 3. A minority of untested relatives did not know whether a family mutation had or had not been found in their tested family member or what their actual test result was. This implies either a lack of disclosure or that woman did not understand the rationale for and significance of testing for a family mutation. 4. Three relatives did not understand a positive result was a mutation. Conclusion: The implication of this research for breast cancer counselling and testing services is that women who wait for counselling are no worse off in terms of short- or long-term general psychological distress than women who receive the intervention early. There is a suggestion that unaffected women without the disease found counselling more advantageous than affected women. The meaning of BRCA results as reported by women is concerning particularly women's understanding of negative and inconclusive results and further research is needed in this area. Too much information presented at counselling may affect women's comprehension of risk, BRCA testing and future test results and further research is required to evaluate the effects of information overload.

L’imagerie basée sur la diffraction des rayons X est une technique non-invasive puissante pour l’identification et caractérisation de matériaux différents. Comparée aux techniques traditionnelles utilisant la transmission des rayons X, elle permet d’extraire des informations beaucoup plus caractéristiques pour le matériau inspecté, comme les positions des pics de Bragg pour des matériaux cristallins et le facteur de forme moléculaire pour les matériaux amorphes. Le potentiel de cette méthode a été reconnu par de nombreuses équipes de recherche et de nombreuses applications comme l’inspection de bagage, le contrôle non-destructif, la détection de drogue et la caractérisation de tissus biologiques ont été proposées. La méthode par dispersion d’énergie (EDXRD) est particulièrement adaptée à ce type d’application car elle permet l’utilisation d’un tube à rayons X conventionnel, l’acquisition du spectre entier en une fois et des architectures parallélisées pour l’inspection d’un objet entier en un temps raisonnable. L’objectif de ce travail est d’optimiser toute la chaîne de caractérisation. L’optimisation comprend deux aspects : l’optimisation du système d’acquisition et du traitement des données. La dernière concerne particulièrement la correction des spectres de diffraction dégradés par le processus d’acquisition. Des méthodes de reconstruction sont proposées et validées sur des spectres simulés et expérimentaux. L’optimisation du système est réalisée en utilisant des facteurs de mérite comme l’efficacité quantique de détection (DQE), le rapport contraste sur bruit (CNR) et les courbes de caractéristiques opérationnelles de réception (ROC).La première application choisie, c’est l’imagerie du sein basée sur la diffraction qui a pour but de distinguer des tissus cancéreux des tissus sains. Deux configurations de collimation sans multiplexage combinant EDXRD et ADXRD sont proposées suite au processus d’optimisation. Une étude de simulation du système entier et d’un fantôme de sein a été réalisée afin de déterminer la dose requise pour la détection d’un petit carcinome de 4 mm. La deuxième application concerne la détection de matériaux illicites pendant le contrôle de sécurité. L’intérêt possible d’un système de collimation multiplexé a été étudié. / X-ray diffraction imaging is a powerful noninvasive technique to identify or characterize different materials. Compared to traditional techniques using X-ray transmission, it allows to extract more material characteristic information, such as the Bragg peak positions for crystalline materials as well as the molecular form factor for amorphous materials. The potential of this technique has been recognized by many researchers and numerous applications such as luggage inspection, nondestructive testing, drug detection and biological tissue characterization have been proposed.The method of energy dispersive X-ray diffraction (EDXRD) is particularly suited for this type of applications as it allows the use of a conventional X-ray tube, the acquisition of the whole spectrum at the same time and parallelized architectures to inspect an entire object in a reasonable time. The purpose of the present work is to optimize the whole material characterization chain. Optimization comprises two aspects: optimization of the acquisition system and of data processing. The last one concerns especially the correction of diffraction pattern degraded by acquisition process. Reconstruction methods are proposed and validated on simulated and experimental spectra. System optimization is realized using figures of merit such as detective quantum efficiency (DQE), contrast to noise ratio (CNR) and receiver operating characteristic (ROC) curves.The first chosen application is XRD based breast imaging which aims to distinguish cancerous tissues from healthy tissues. Two non-multiplexed collimation configurations combining EDXRD and ADXRD are proposed after optimization procedure. A simulation study of the whole system and a breast phantom was realized to determine the required dose to detect a 4 mm carcinoma nodule. The second application concerns detection of illicit materials during security check. The possible benefit of a multiplexed collimation system was examined.

Made available in DSpace on 2014-12-17T14:55:04Z (GMT). No. of bitstreams: 1 GlauciaRMAS_TESE.pdf: 2163942 bytes, checksum: 5778dd8818ffc286b87137c2a56b9fc0 (MD5) Previous issue date: 2012-05-14 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA. / O c?ncer de mama, apesar de ser uma das principais causas de morte entre as mulheres em todo o mundo, ? uma doen?a que pode ser curada se for diagnosticada precocemente. Uma das principais t?cnicas utilizadas na detec??o de c?ncer de mama ? a Fine Needle Aspirate FNA (ou Pun??o Aspirativa por Agulha Fina) que, dependendo do caso cl?nico, necessita da an?lise de v?rios m?dicos especialistas para a efetiva??o do diagn?stico. Entretanto, a realiza??o de tais diagn?sticos e a emiss?o de segundos pareceres t?m sido prejudicadas pela dispers?o geogr?fica dos m?dicos e/ou a dificuldade na concilia??o de tempo para realizar trabalhos em conjunto. Inserindo-se nessa realidade, esta tese de doutorado utiliza intelig?ncia computacional no apoio ? tomada de decis?o m?dica para a realiza??o de telediagn?sticos. Para tanto apresenta um m?todo fuzzy destinado a auxiliar o diagn?stico de c?ncer de mama, capaz de processar e classificar dados extra?dos de esfrega?os de tecidos mam?rios obtidos por FNA. Este m?todo est? integrado a um ambiente virtual para realiza??o de telediagn?stico colaborativo, cujo modelo foi desenvolvido prevendo a incorpora??o de M?dulos de Pr?-Diagn?stico para apoio ? tomada de decis?o m?dica. No desenvolvimento do m?todo fuzzy, o processo de aquisi??o do conhecimento foi realizado pela extra??o e an?lise dos dados num?ricos em base de dados padr?o ouro e por entrevistas e discuss?es com m?dicos especialistas. O m?todo foi testado e validado com casos reais e, em fun??o da sensibilidade e da especificidade alcan?adas (diagn?stico correto de tumores, respectivamente, malignos e benignos), os resultados obtidos foram satisfat?rios, considerando tanto os pareceres de m?dicos e os padr?es de qualidade para diagn?stico de c?ncer de mama quanto a compara??o com outros estudos realizados envolvendo diagn?stico de c?ncer de mama por FNA.