The purpose of this work was to develop novel optical imaging technology and algorithms as a nondestructive method for detection and diagnosis of cancer in breast specimens. There are many ways in which the diagnosis of disease can benefit from fast and intelligent optical imaging technology. Our existing ability to provide this diagnosis depends on time-consuming pathology analysis. Optical coherence tomography (OCT) is a non-invasive optical imaging modality that provides depth-resolved, high-resolution images of tissue microstructure in real-time. OCT could provide a rapid evaluation of specimens while patients are still in the office, and has strong potential to improve the efficiency in evaluation of breast pathology specimens (biopsy or surgical). In this work, we demonstrate an imaging system to address this unmet clinical need, artificial intelligence algorithms to interpret the images, and early work towards miniaturizing the technology. We present an OCT system that achieves a line scan rate of 250kHz, meaning we can image a pathology cassette in 41 seconds, which is more than double the fastest scan rate in the field. By utilizing a multiplexed superluminescent diode (SLD) light source, which has strong noise performance over imaging speed, we achieve high resolution imaging under 5 um in tissue (axially and laterally). The system features a 1.1 mm 6-dB sensitivity fall-off range when imaging at 250 kHz. The scanner features large-area scanning with the implementation of a 2-axis motorized stage, enabling visualization of areas up to 10 cm x 10 cm (prior work visualizes 3 mm x 3mm). We showcase the results of demonstrating the performance of this system on a 100-patient clinical imaging study of breast biopsies, as well as imaging of clinical pathology specimens from the breast, prostate, lung, and pancreas in an IRB-approved study. Further, we show our work towards developing artificial intelligence (AI) for cancer detection within OCT images. Using retrospective data, we developed a type of AI algorithm known as a convolutional neural network (CNN) to classify OCT images of breast tissue from 49 patients. The binary cancer classification achieved 94% accuracy, 96% sensitivity, and 92% specificity. This framework had higher accuracy than the 88% accuracy of 7 clinician readers combined in our lab’s earlier multi-reader study. Lastly, we demonstrate a supercontinuum light source based on a 1 mm2 Si3N4 photonic chip for OCT imaging that has better performance than the state-of-the-art laser. Existing broadband laser sources for OCT are large, bulky, and have high excess noise. Our Si3N4 chip fundamentally eliminates the excess noise common to lasers and achieves 105 dB sensitivity and 1.81 mm 6-dB sensitivity roll-off with only 300 µW power on the sample.

Decision support systems have emerged over five decades ago to serve decision makers in uncertain conditions and usually rapidly changing and unstructured problems. Most decision support approaches, such as Bayesian decision theory and computing with words, compare and analyze the consequences of different decision alternatives. Bayesian decision methods use probabilities to handle uncertainty and have been widely used in different areas for estimating, predicting, and offering decision supports. On the other hand, computing with words (CW) and approximate reasoning apply fuzzy set theory to deal with imprecise measurements and inexact information and are most concerned with propositions stated in natural language. The concept of a Z-number [69] has been recently introduced to represent propositions and their reliability in natural language. This work proposes a methodology that integrates Z-numbers and Bayesian decision theory to provide decision support when precise measurements and exact values of parameters and probabilities are not available. The relationships and computing methods required for such integration are derived and mathematically proved. The proposed hybrid methodology benefits from both approaches and combines them to model the expert knowledge and its certainty (reliability) in natural language and apply such model to provide decision support. To the best of our knowledge, so far there has been no other decision support methodology capable of using the reliability of propositions in natural language. In order to demonstrate the proof of concept, the proposed methodology has been applied to a realistic case study on breast cancer diagnosis and a daily life example of choosing means of transportation.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

Le cancer du sein demeure un problème de santé publique majeure dans le monde. Afin d'améliorer les chances de survie et la qualité de vie des femmes, il est nécessaire d’effectuer le diagnostic à un stade précoce et d’appliquer le traitement. Dans ce contexte, un des objectifs de cette thèse est de développer des puces à protéines pour le diagnostic et le pronostic du cancer du sein. Parmi les nombreux marqueurs biologiques potentiels, des recherches récentes ont montré que des anticorps anti-heat shock proteins (anti-HSPs) sont associés à la genèse tumorale. Ces anticorps seraient donc de bons biomarqueurs diagnostiques et pronostiques pour le cancer du sein. Par conséquent, nous avons élaboré une puce à antigènes afin de détecter les anticorps anti-HSP dans le sérum de 50 patients atteints de cancer du sein et de 26 témoins sains. Nos résultats indiquent clairement que la la détection multiplex d’une combinaison d'anticorps anti-HSP permet de discriminer les patients atteints de cancer du sein des témoins sains avec une sensibilité de 86% et une spécificité de 100%. Ensuite, nous avons élaboré une puce à anticorps pour doser la concentration de l'activateur du plasminogène de type urokinase (uPA) et de son inhibiteur principal (PAI-1) dans 16 extraits cytosoliques de tissus tumoraux. uPA et PAI-1 sont décrits comme étant de bons biomarqueurs pronostiques et prédictifs du cancer du sein. De faibles taux de uPA (≤3 ng / mg de protéine) et PAI-1 (≤14 ng / mg de protéine) sont associés à un faible risque de récidive et pas de bénéfice d’une chimiothérapie pour les patients atteints de cancer du sein. Les résultats obtenus à partir de puces à anticorps étaient surface dépendante par rapport aux résultats obtenus sous forme ELISA. En outre, l'utilisation de nos puces à anticorps nécessite 25 fois moins de volume d'échantillon par rapport à un dosage ELISA, résolvant ainsi les principales limites de la méthode ELISA. Enfin, nous avons déterminé et optimisé les paramètres influençant les performances des puces à protéines, comme par exemple la chimie de surface, la durée expérimentale, la concentration des solutions, etc. Nous avons également étudié les conditions de stockage à la fois pour des surfaces chimiquement fonctionnalisées et pour les puces à protéines. Les résultats ont montré que les puces à protéines conservent leur activité biologique jusqu’à trois mois de stockage. / Breast cancer becomes the most common cancer among women. In order to improve women's chances of survival and life quality, to be diagnosed at an early stage and to receive correct treatment are the most promising ways. In this context, we aim at developing an antigen microarray for screening serological biomarkers to diagnose breast cancer patients as early as possible. Among numerous potential biomarkers, recent researches showed that antibodies against heat shock proteins (HSPs) are associated with tumor genesis and would be good diagnostic and prognostic biomarkers for breast cancer. Therefore, we used customized antigen microarray to screen anti-HSP antibodies in 50 breast cancer patients and 26 healthy controls. Our results indicated clearly that combining multiplex detection of anti-HSPs antibodies could discriminate breast cancer patients from healthy controls with sensitivity 86% and specificity 100%. Then, we elaborated an antibody microarray to detect the concentration of urokinase type plasminogen activator (uPA) in 16 cytosolic extracts of breast tummor tissue. uPA is good prognostic and predictive biomarker for breast cancer, low levels of uPA (≤3 ng/mg of protein) is associated with low risk of recurrence and no benefit of chemotherapy for breast cancer patients, and vice versa. Our results showed that the results obtained from our antibody microarray were surface dependent compared with the results obtained from ELISA. Furthermore, the use of our antibody microarray requires 25 times less sample volume compared with ELISA kit, thus solving the main limitations of ELISA. Finally, we determined and optimized the parameters which affected the performances of protein microarray, e.g. microarray surface chemistry, experimental duration, the concentration of solutions, etc. Furthermore, we studied the storage conditions for both chemically functionalized microarray surface as well as printed protein microarray. Results showed that our protein microarrays retain efficient biological activity for at least 3 month of storage.

Karlsson, E & Liljesson, M. Kvinnors upplevelse av att få en bröstcancerdiagnos. Examensarbete i omvårdnad 15 högskolepoäng. Malmö universitet: Fakulteten för Hälsa och samhälle, Institutionen för vårdvetenskap, 2022.   Bakgrund: Bröstcancer är den vanligaste cancerformen för kvinnor världen över. I Sverige diagnostiserades 7570 kvinnor med bröstcancer år 2020. Bröstcancern upptäcks vanligtvis av kvinnan själv eller genom mammografi. Diagnostiken består av trippeldiagnostik, som kan fastställa diagnosen. Den generella behandlingen av bröstcancer är cytostatika, kirurgiskt ingrepp och/eller strålning. Att genomgå en behandling är fysiskt, psykiskt och socialt utmanande. Att diagnostiseras med en sjukdom kan innefatta en kris eftersom det utgör ett hot mot individens existens. Syfte: Litteraturstudiens syfte var att belysa biologiska kvinnors upplevelse av att få en bröstcancerdiagnos. Metod: En kvalitativ litteraturstudie har utförts med inriktning mot omvårdnad. Litteratursökningar har genomförts i databaserna Cinahl och PubMed där 15 kvalitativa artiklar påträffades. Kvalitetsgranskning av artiklarna utfördes enligt SBU:s (2014) mall. En analys av artiklarna har utformats enligt Friberg (2017). Resultat: Att få en bröstcancerdiagnos upplevs av majoriteten av kvinnorna som en chock och livet förändrades. Kvinnorna uttryckte olika existentiella tankar och känslor som gav påverkan på det fysiska, psykiska och sociala. Resultatet identifierade följande kategorier känslomässiga reaktioner, tankar kring döden, socialt stöd, religionens betydelse kopplat till diagnosen och förlust av identitet. Konklusion; Att få en bröstcancerdiagnos upplevdes av majoriteten av kvinnorna som en chock medan tankar och känslor utspelade sig olika för varje individ. Genom kvinnornas upplevelse kan krisreaktionens två första faser identifieras, vilket gör att sjuksköterskan behöver vara medveten och kunnig om krisreaktionens olika faser för att kunna utföra en god omvårdnad. / Karlsson, E & Liljesson, M. Women's experience of being diagnosed with breast cancer. Degree Project in nursing 15 credit points. Malmö University: Faculty of Health and Society, Department of Care Science, 2022.  Background: Breast cancer is the most common form of cancer worldwide among women. In 2020, 7570 women were diagnosed with breast cancer. Breast cancer is usually detected by the woman herself or by mammography. By preforming a triple diagnosis, the diagnosis will establish. The general treatment of breast cancer is chemotherapy, surgery, and radiation. Undergoing treatment is physically, psychological, and socially challenging. Being diagnosed with a disease can create a crisis as the disease poses a threat to the individual's existence. Aim: The purpose of the literature review is to illustrate biological women's experience of being diagnosed with breast cancer. Method: A qualitative literature review has been conducted with a focus on nursing. Literature searches have been performed in the databases Cinahl and PubMed, where 15 qualitative articles were found. Qualitative review of the articles was performed following SBU:s (2014) template. An analysis of the articles has been designed according to Friberg (2017). Results: Getting a breast cancer diagnosis is perceived by most women as a shock and their lives changed. The women expressed various existential thoughts and feelings that gave physically, psychological, and socially impact. The result identified following categories emotional reactions, thoughts about death, social support, the religious meaning connected to the diagnosis and loss of identity. Conclusion: The women's experience of receiving the diagnosis for breast cancer was experienced by the majority as a shock, while thoughts and feelings unfolded differently for each individual. Through the women´s experience, the first two phases of crisis can be identified. Which means that the nurses need to be aware and knowledgeable about the different phases of the crisis to be able to perform good nursing.

Inledning: Med tiden har folkhälsa och vad som ses som folksjukdomar förändrats. Idag har vi folksjukdomar som inte var vanliga för årtionden sedan, sjukdomar som nu blivit ett globalt folkhälsoproblem. En av dessa sjukdomar är cancer. Bröstcancer är den vanligaste cancerform för kvinnor. Brösten är en stor del för att en kvinna ska känna sig kvinnlig. Syfte: Syftet med denna litteraturstudie är att beskriva kvinnans psykiska hälsa efter en bröstcancerdiagnos med fokus på ångest, depression och stress. Metod: Vald metod är en systematisk litteraturöversikt. Artiklar söktes i databasen PubMed som analyserades systematiskt. Under sökprocessen valdes 15 vetenskapliga artiklar ut med relevant information för att svara på syftet till studien. Tre teman valdes ut under analysen av artiklarna. Temaorden: Rädsla för återfall av bröstcancer, kvinnlighet, psykologiska hälsoeffekter. Resultat: Resultatet visar att kvinnor känner sig mindre kvinnlig och attraktiv när ett bröst opererats bort. Kvinnor med en bröstcancerhistorik är rädd för att få ett återfall vilket påverkar deras liv genom oro, ångest och stress. Yngre kvinnor är mer rädd för döden än äldre vilket kan bero på, yngre kvinnor har små barn och är rädd för att inte se barnen växa upp. Diskussion: En bröstcancerdiagnos kan vända upp och ner på tillvaron för en kvinna. Efter att hon har fått en diagnos behöver hon ändra om sin planering inför framtiden Detta kan skapa ångest och oro inför hur det kommer att bli, hur hon kommer att må samt oro för vilken utgång sjukdomen har. Det finns ett behov för mer träning för läkaren och sjuksköterskor för att bättre hjälpa bröstcancerpatienter med psykologiska konsekvenser. / Introduction: Over time, disease patterns affecting the population have changes. Today, many people are diagnosed with diseases that were not common decades ago, which have now become a global public health problem. One of these diseases is cancer. Breast cancer is the most common cancer form for women, and stressful in part because of the association of breasts with femininity.   Purpose: The purpose of this literature study is to describe the mental health of the woman after a breast cancer diagnosis focusing on anxiety, depression and stress. Method: The chosen method is a systematic literature review. Articles have been systematically searched in the PubMed database. During the search process, 15 scientific articles were selected with relevant information to respond to the purpose of the study. Three themes emerged during the analysis of the articles: Fear of breast cancer recurrence, femininity, and psychological health effects. Result: The results show that women feel less feminine and attractive when a breast has been removed. Women with breast cancer history are afraid of recurrence which affects their lives through anxiety, anxiety and stress. Younger women are more afraid of death than older women, which may be because younger women have young children and are fearful of not seeing them grow up. Discussion: A breast cancer diagnosis can strongly affect a woman. After a woman's diagnosis, she needs to change her planning for the future. This can create anxiety and anxiety about recovery and how she will feel and lead to worry about the outcome of the disease. There is a need for more training for doctors and nurses to better help breast cancer patients who experience stress, anxiety and depression.

Breast cancer is the most common cancer in women. It is a highly heterogeneous disease in terms of histology, therapeutic response and patient outcomes. Early and accurate detection of breast cancer is crucial as the patient prognosis varies greatly depending on the diagnosis of the disease. Nonetheless current breast cancer classification methods fail to precisely sub-classify the disease, resulting in potential inadequate therapeutic management of patients and subsequent poor clinical outcomes. Substantial effort is therefore put in cancer research to develop methods and find new biomarkers efficiently identifying and characterizing breast tumor cells. Moreover it is now well-recognized that the intensive cross-talk between cancer cells and their microenvironment (including non-tumor cells) highly influences cancer progression. Recently, a growing body of clinical evidence reported the prognostic and predictive value associated with the presence of tumor infiltrating lymphocytes (TILs) in the microenvironment of breast tumors. Although the evaluation of TILs would be of great value for the management of patients and the development of new immunotherapies, it is currently not assessed in routine practice. Furthermore Fourier transform infrared (FTIR) imaging has shown its usefulness to study a panel of human cancers. Infrared (IR) spectroscopy coupled to microscopy provides images composed of multiple spectra reflecting the biochemical composition and subtle modifications within biological samples. IR imaging therefore provides useful information to improve breast cancer identification and characterization. The ultimate aim of this thesis is to improve breast cancer diagnosis using FTIR imaging to better identify and characterize cancer cells and the tumor microenvironment of breast cancers. In a first step we carried out a feasibility study aiming at evaluating the impact of the sample fixation process on IR spectra. While spectra were undeniably influenced by this biochemical alteration, our results indicated that closely-related cell types were influenced similarly and could still be discriminated on the basis of their spectral features. We then demonstrated the capability of IR imaging to discriminate a tumor from a normal tissue environment based on the spectral features of tumor cells and the surrounding extracellular matrix. A particular focus was placed on the identification of lymphocyte spectral signatures of cells isolated from blood or present within secondary lymphoid organs such as tonsils. Our results revealed that IR imaging was sensitive enough to discriminate lymphocyte subpopulations and to identify a particular spectral signature that we assigned to lymphocyte activation. Finally we highlighted the potential value of IR imaging as complementary tool to identify and characterize TILs in breast tumor samples. Altogether, our results suggest that IR imaging provides interesting and reliable information to improve breast cancer characterization and to assess the immune microenvironment of breast tumors.<p>/<p>Le cancer du sein est le carcinome le plus fréquent chez la femme. C’est une maladie très hétérogène du point de vue histologique, de la réponse thérapeutique et de l’évolution clinique. Une détection rapide et précise de la maladie est cruciale, un diagnostic du cancer du sein dès les premiers stades de la maladie permet une meilleure prise en charge du patient et est directement associé à un meilleur pronostic. Néanmoins la classification actuelle des cancers du sein ne permet souvent pas de caractériser la maladie de manière précise, ce qui donne lieu à la mise en place de traitements moins ciblés et une évolution clinique peu favorable. Pour remédier à cela, des efforts conséquents sont réalisés en recherche, dans le but de mettre au point des méthodes capables d’identifier et de caractériser les cellules tumorales. De plus il est actuellement reconnu que le micro-environnement tumoral (composé des cellules non-tumorales) influence fortement la progression du cancer. Récemment de nombreuses études ont montré que la présence de lymphocytes au niveau des tumeurs mammaires (TILs) était corrélée à un meilleur facteur pronostic et prédictif. Bien que l’évaluation des TILs soit de grande importance dans le cadre des immunothérapies, cet élément n’est actuellement pas pris en compte dans les analyses de routine. Par ailleurs, l’imagerie infrarouge par transformée de Fourier (FTIR) a démontré son utilité dans l’étude de plusieurs cancers humains. La spectroscopie infrarouge (IR) couplée à la microscopie fourni des images composées de multiples spectres qui reflètent la composition biochimique et les modifications dans les échantillons biologiques. De ce fait l’imagerie infrarouge procure des informations utiles pour améliorer l’identification et la caractérisation du cancer du sein. L’objectif général de cette thèse est d’améliorer le diagnostic du cancer du sein par imagerie FTIR pour mieux identifier et caractériser les cellules cancéreuses et le micro-environnement tumoral des tumeurs mammaires. Dans un premier temps nous avons effectué une étude de faisabilité afin d’évaluer l’impact du protocole de fixation des tissus sur les spectres IR. Bien que les spectres soient indéniablement influencés par cette altération biochimique, nos résultats indiquent que des types cellulaires proches sont influencés de manière similaire et peuvent donc être discriminés sur base de leurs caractéristiques spectrales. Nous avons ensuite démontré la capacité de l’imagerie IR de distinguer un environnement tumoral d’un environnement normal sur base des particularités spectrales des cellules tumorales et de la matrice extracellulaire. Une attention particulière a ensuite été portée afin d’identifier des signatures spectrales de cellules immunitaires du sang et au sein d’organes lymphoïdes secondaires, tels que les amygdales. Nos résultats ont révélé que l’imagerie IR permet d'identifier une signature spectrale particulière, que nous avons associée à une stimulation lymphocytaire. Finalement nous avons mis en évidence l’utilité de l’imagerie IR en tant qu’outil complémentaire pour identifier et caractériser les TILs dans les échantillons tumoraux mammaires. De manière générale, nos résultats suggèrent que l’imagerie IR fournit des informations intéressantes et fiables pour améliorer la caractérisation et l’évaluation du micro-environnement immunitaire dans les tumeurs mammaires. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Background. While the prevalence of a pathogenic variant in the BRCA1 and BRCA2 genes occurs in about 1:400 (0.25%) in the general population, the prevalence is as high as 1:40 (2.5%) among the Ashkenazi Jewish population. Despite cost-effective preventive measures for mutation carriers, Orthodox Jews constitute a cultural and religious group that presents challenges to BRCA1 and BRCA2 genetic testing. This study analyzed a dialogue of key stakeholders and community members to explore factors that influence decision-making about BRCA1 and BRCA2 genetic testing in the New York Orthodox Jewish community. Methods. Qualitative research methods, based in Grounded Theory and Narrative Research, were utilized to analyze the narratives of key stakeholders and community members in an analysis of qualitative data collected from 49 stakeholders. A content analysis was conducted to identify themes; inter-rater reliability was 71%. Results. Facilitators to genetic testing were prevention and education, while barriers to genetic testing included negative emotions, impact on family/romantic relationships, cost, and stigma. The role of religious figures and healthcare professionals in medical decision-making were viewed as controversial. Education, health, and community were discussed as influential factors. There were issues around disclosure, implementation, and information needs. Conclusion. This study revealed the voices of the Orthodox Jewish women (decision-makers) and key stakeholders (influencers) who play a critical role in the medical decision-making process. The findings have broad implications for engaging community stakeholders within faith-based or culturally distinct groups to ensure better utilization of healthcare services for cancer screening and prevention designed to improve population health.