O câncer é responsável por cerca de 7 milhões de óbitos anuais em todo o mundo. Estima-se que 25% de todos os cânceres são de pele, sendo no Brasil o tipo mais incidente em todas as regiões geográficas. Entre eles, o tipo melanoma, responsável por 4% dos cânceres de pele, cuja incidência dobrou mundialmente nos últimos dez anos. Entre os métodos diagnósticos empregados, cita-se a regra ABCD, que leva em consideração assimetria (A), bordas (B), cor (C) e diâmetro (D) de manchas ou nevos. O processamento digital de imagens tem mostrado um bom potencial para auxiliar no diagnóstico precoce de melanomas. Neste sentido, o objetivo do presente estudo foi desenvolver um software, na plataforma MATLAB®, associado a um hardware para padronizar a aquisição de imagens, visando realizar o diagnóstico e acompanhamento de lesões cutâneas suspeitas de malignidade (melanoma). Utilizou-se como norteador a regra ABCD para o desenvolvimento de métodos de análise computacional. Empregou-se o MATLAB como ambiente de programação para o desenvolvimento de um software para o processamento digital de imagens. As imagens utilizadas foram adquiridas de dois bancos de imagens de acesso livre. Foram inclusas imagens de melanomas (n=15) e imagens nevos (não câncer) (n=15). Utilizaram-se imagens no canal de cor RGB, as quais foram convertidas para escala de cinza, aplicação de filtro de mediana 8x8 e técnica de aproximação por vizinhança 3x3. Após, procedeu-se a binarização e inversão de preto e branco para posterior extração das características do contorno da lesão. Para a aquisição padronizada de imagens foi desenvolvido um protótipo de hardware, o qual não foi empregado neste estudo (que utilizou imagens com diagnóstico fechado, de bancos de imagem), mas foi validado para a avaliação do diâmetro das lesões (D). Utilizou-se a estatística descritiva onde os grupos foram submetidos ao teste não paramétrico para duas amostras independentes de Mann-Whitney U. Ainda, para avaliar a sensibilidade (SE) e especificidade (SP) de cada variável, empregou-se a curva ROC. O classificador utilizado foi uma rede neural artificial de base radial, obtendo acerto diagnóstico para as imagens melanomas de 100% e para imagens não câncer de 90,9%. Desta forma, o acerto global para predição diagnóstica foi de 95,5%. Em relação a SE e SP do método proposto, obteve uma área sob a curva ROC de 0,967, o que sugere uma excelente capacidade de predição diagnóstica, sobretudo, com baixo custo de utilização, visto que o software pode ser executado na grande maioria dos sistemas operacionais hoje utilizados. / Cancer is responsible for about 7 million deaths annually worldwide. It is estimated that 25% of all cancers are skin, and in Brazil the most frequent in all geographic regions type. Among them, the melanoma type, accounting for 4% of skin cancers, whose incidence has doubled worldwide in the past decade. Among the diagnostic methods employed, it is cited ABCD rule which considers asymmetry (A), edges (B), color (C) and diameter (D) stains or nevi. The digital image processing has shown good potential to aid in early diagnosis of melanoma. In this sense, the objective of this study was to develop software in MATLAB® platform, associated with hardware to standardize image acquisition aiming at performing the diagnosis and monitoring of suspected malignancy (melanoma) skin lesions. Was used as the ABCD rule for guiding the development of methods of computational analysis. We used MATLAB as a programming environment for the development of software for digital image processing. The images used were acquired two banks pictures free access. Images of melanomas (n = 15) and pictures nevi (not cancer) (n = 15) were included. We used the image in RGB color channel, which were converted to grayscale, application of 8x8 median filter and approximation technique for 3x3 neighborhood. After we preceded binarization and reversing black and white for subsequent feature extraction contours of the lesion. For the standardized image acquisition was developed a prototype hardware, which was not used in this study (that used with enclosed diagnostic images of image banks), but has been validated for evaluation of lesion diameter (D). We used descriptive statistics where the groups were subjected to non-parametric test for two independent samples Mann-Whitney U test yet, to evaluate the sensitivity (SE) and specificity (SP) of each variable, we used the ROC curve. The classifier used was an artificial neural network with radial basis function, obtaining diagnostic accuracy for melanoma images and 100% for images not cancer of 90.9%. Thus, the overall diagnostic accuracy for prediction was 95.5%. Regarding the SE and SP of the proposed method, obtained an area under the ROC curve of 0.967, which suggests an excellent diagnostic ability to predict, especially with low costs, since the software can be run in most systems operational use today.

O câncer é responsável por cerca de 7 milhões de óbitos anuais em todo o mundo. Estima-se que 25% de todos os cânceres são de pele, sendo no Brasil o tipo mais incidente em todas as regiões geográficas. Entre eles, o tipo melanoma, responsável por 4% dos cânceres de pele, cuja incidência dobrou mundialmente nos últimos dez anos. Entre os métodos diagnósticos empregados, cita-se a regra ABCD, que leva em consideração assimetria (A), bordas (B), cor (C) e diâmetro (D) de manchas ou nevos. O processamento digital de imagens tem mostrado um bom potencial para auxiliar no diagnóstico precoce de melanomas. Neste sentido, o objetivo do presente estudo foi desenvolver um software, na plataforma MATLAB®, associado a um hardware para padronizar a aquisição de imagens, visando realizar o diagnóstico e acompanhamento de lesões cutâneas suspeitas de malignidade (melanoma). Utilizou-se como norteador a regra ABCD para o desenvolvimento de métodos de análise computacional. Empregou-se o MATLAB como ambiente de programação para o desenvolvimento de um software para o processamento digital de imagens. As imagens utilizadas foram adquiridas de dois bancos de imagens de acesso livre. Foram inclusas imagens de melanomas (n=15) e imagens nevos (não câncer) (n=15). Utilizaram-se imagens no canal de cor RGB, as quais foram convertidas para escala de cinza, aplicação de filtro de mediana 8x8 e técnica de aproximação por vizinhança 3x3. Após, procedeu-se a binarização e inversão de preto e branco para posterior extração das características do contorno da lesão. Para a aquisição padronizada de imagens foi desenvolvido um protótipo de hardware, o qual não foi empregado neste estudo (que utilizou imagens com diagnóstico fechado, de bancos de imagem), mas foi validado para a avaliação do diâmetro das lesões (D). Utilizou-se a estatística descritiva onde os grupos foram submetidos ao teste não paramétrico para duas amostras independentes de Mann-Whitney U. Ainda, para avaliar a sensibilidade (SE) e especificidade (SP) de cada variável, empregou-se a curva ROC. O classificador utilizado foi uma rede neural artificial de base radial, obtendo acerto diagnóstico para as imagens melanomas de 100% e para imagens não câncer de 90,9%. Desta forma, o acerto global para predição diagnóstica foi de 95,5%. Em relação a SE e SP do método proposto, obteve uma área sob a curva ROC de 0,967, o que sugere uma excelente capacidade de predição diagnóstica, sobretudo, com baixo custo de utilização, visto que o software pode ser executado na grande maioria dos sistemas operacionais hoje utilizados. / Cancer is responsible for about 7 million deaths annually worldwide. It is estimated that 25% of all cancers are skin, and in Brazil the most frequent in all geographic regions type. Among them, the melanoma type, accounting for 4% of skin cancers, whose incidence has doubled worldwide in the past decade. Among the diagnostic methods employed, it is cited ABCD rule which considers asymmetry (A), edges (B), color (C) and diameter (D) stains or nevi. The digital image processing has shown good potential to aid in early diagnosis of melanoma. In this sense, the objective of this study was to develop software in MATLAB® platform, associated with hardware to standardize image acquisition aiming at performing the diagnosis and monitoring of suspected malignancy (melanoma) skin lesions. Was used as the ABCD rule for guiding the development of methods of computational analysis. We used MATLAB as a programming environment for the development of software for digital image processing. The images used were acquired two banks pictures free access. Images of melanomas (n = 15) and pictures nevi (not cancer) (n = 15) were included. We used the image in RGB color channel, which were converted to grayscale, application of 8x8 median filter and approximation technique for 3x3 neighborhood. After we preceded binarization and reversing black and white for subsequent feature extraction contours of the lesion. For the standardized image acquisition was developed a prototype hardware, which was not used in this study (that used with enclosed diagnostic images of image banks), but has been validated for evaluation of lesion diameter (D). We used descriptive statistics where the groups were subjected to non-parametric test for two independent samples Mann-Whitney U test yet, to evaluate the sensitivity (SE) and specificity (SP) of each variable, we used the ROC curve. The classifier used was an artificial neural network with radial basis function, obtaining diagnostic accuracy for melanoma images and 100% for images not cancer of 90.9%. Thus, the overall diagnostic accuracy for prediction was 95.5%. Regarding the SE and SP of the proposed method, obtained an area under the ROC curve of 0.967, which suggests an excellent diagnostic ability to predict, especially with low costs, since the software can be run in most systems operational use today.

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.

Varje år diagnostiseras över 50 000 individer med cancer i Sverige. Ett cancerbesked väcker blandade känslor och associeras ofta med lidande och död. Ett svårt besked kan leda till en förändrad livssituation och kan även ses som början på en lång och mödosam resa. En vetenskaplig litteraturstudie baserad på 15 originalartiklar genomfördes med syftet att belysa patientens upplevelse av ett cancerbesked och därmed öka sjuksköterskans förståelse för patientens situation samt fördjupa kunskaperna inom ämnet. Genom litteraturgranskningen identifierades tre teman: information, emotionella reaktioner samt psykosocialt stöd. Patienten upplever att det är viktigt att informationen ges på ett öppet och ärligt sätt. Vidare framkom det betydelsefullt att uppmärksamma patientens emotionella reaktioner i samband med beskedet. Eftersom upplevelsen av ett cancerbesked påverkar patientens fortsatta upplevelse av sin sjukdom, har sjuksköterskan en viktig roll att fylla genom att erbjuda psykosocialt stöd i de olika tänkbara situationer som kan uppkomma i samband med ett livsavgörande besked. Fortsatt forskning behövs för att jämföra hur de rekommendationer som finns angående delgivandet av ett svårt besked överensstämmer med patienternas egna upplevelser och önskemål. / Every year, over 50 000 individuals in Sweden are diagnosed with cancer. The disclosure of the cancer diagnosis arouses emotions and is often associated with suffering and death. Receiving bad news may lead to changes in life and can also be seen as the beginning of a long and difficult journey. A scientific study based on 15 original articles was carried out with the purpose to identify the patient’s experience of receiving a cancer diagnosis and therefore increase the nurse’s understanding for the patient’s situation and deepen the knowledge of the subject. When examining the articles three themes were identified: information, emotional reactions and psychosocial support. The patient experience that it is important that the information is given in an open and honest manner. Patients also find it important that their emotional reaction is being observed as receiving the diagnosis. As the disclosure of the cancer diagnosis affects the patient’s further perception of the disease the nurse has an important role providing psychosocial support in various situations that may arise in connection with the disclosure. Continued research is needed to compare how guidelines for giving bad news to a patient correspond with the patient’s own experiences and preferences.

<p>Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping healthcare in many ways and so in the diagnosis of skin lesion. Computer aided diagnosis help clinicians in detecting the cancer. The study was conducted to classify the seven classes of skin lesion using very powerful convolutional neural networks. The two pre trained models i.e., DenseNet and Incepton-v3 were employed to train the model and accuracy, precision, recall, f1score and ROC-AUC was calculated for every class prediction. Moreover, gradient class activation maps were also used to aid the clinicians in determining what are the regions of image that influence model to make a certain decision. These visualizations are used for explainability of the model. Experiments showed that DenseNet performed better then Inception V3. Also it was noted that gradient class activation maps highlighted different regions for predicting same class. The main contribution was to introduce medical aided visualizations in lesion classification model that will help clinicians in understanding the decisions of the model. It will enhance the reliability of the model. Also, different optimizers were employed with both models to compare the accuracies.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Cancer is still among those diseases that prominently contribute to the numerous deaths that are caused each year. But as technology and research is reaching new zeniths in the present times, cure or early detection of cancer is possible. The detection of rare cells can help understand the origin of many diseases. The current study deals with one such technology that is used for the capture or effective separation of these rare cells called Lab-on-a-chip microchip technology. The isolation and capture of rare cells is a problem uniquely suited to microfluidic devices, in which geometries on the cellular length scale can be engineered and a wide range of chemical functionalizations can be implemented. The performance of such devices is primarily affected by the chemical interaction between the cell and the capture surface and the mechanics of cell-surface collision and adhesion. This study focuses on the fundamental adhesion and transport mechanisms in rare cell-capture microdevices, and explores modern device design strategies in a transport context. The biorheology and engineering parameters of cell adhesion are defined; chip geometries are reviewed. Transport at the microscale, cell-wall interactions that result in cell motion across streamlines, is discussed. We have concentrated majorly on the fluid dynamics design of the chip. A simplified description of the device would be to say that the chip is at micro scale. There are posts arranged on the chip such that the arrangement will lead to a higher capture of rare cells. Blood consisting of rare cells will be passed through the chip and the posts will pose as an obstruction so that the interception and capture efficiency of the rare cells increases. The captured cells can be observed by fluorescence microscopy. As compared to previous studies of using solid microposts, we will be incorporating a new concept of cylindrical shell micropost. This type of micropost consists of a solid inner core and the annulus area is covered with a forest of silicon nanopillars. Utilization of such a design helps in increasing the interception and capture efficiency and reducing the hydrodynamic resistance between the cells and the posts. Computational analysis is done for different designs of the posts. Drag on the microposts due to fluid flow has a great significance on the capture efficiency of the chip. Also, the arrangement of the posts is important to contributing to the increase in the interception efficiency. The effects of these parameters on the efficiency in junction with other factors have been studied and quantified. The study is concluded by discussing design strategies with a focus on leveraging the underlying transport phenomena to maximize device performance.

<p dir="ltr">Pancreatic ductal adenocarcinoma (PDAC) remains one of the most deadliest cancer and despite advancements in cancer therapy, remain highly refractory to treatment, largely due to its desmoplastic tumor microenvironment (TME) characterized by complex interactions among cancer cells and stromal components. Particularly, the PDAC associated coagulation system due to leaky tumor vasculatures plays a pivotal role in reshaping the PDAC stroma and its pathogenesis. Understanding the intricate interplay between tumor cells, stromal cells, and the elevated coagulation pathway elements, including tissue factor, thrombin, and fibrin, is essential for developing effective therapeutic strategies. To address these challenges, this research proposes the engineering of a novel PDAC-associated coagulation system using a microfluidic technology, known as coagulation-on-tumor-microenvironment-on-chip (cT-MOC). The study aims to integrate key coagulation pathways in cT-MOC to investigate pivotal interactions in the PDAC stroma: <i>i)</i> thrombin-protease-activated receptors (PARs) mediated promotion of PDAC fibrosis via activation of cancer-fibroblast cross-talk; <i>ii)</i> in-depth analysis of transport and mechanical properties of collagen-fibrin microstructure; <i>iii)</i> inhibited drug delivery in reprogrammed PDAC stroma due to pronounced fibrin deposition on collagen. By leveraging innovative microfluidic technologies and comprehensive experimental approaches, the research endeavors to provide a novel platform that bridges traditional <i>in vitro</i> and <i>in vivo</i> models to overcome the challenges posed by the desmoplastic TME and enhance therapeutic strategies for treatment by targeting the coagulation at the PDAC TME.</p>

Increasing time trends in the recorded incidence of childhood cancer have been reported in many different settings. The extent to which these trends reflect real changes in incidence, rather than improvements in methods for diagnosis and registration, is controversial. Using data from the National Registry of Childhood Tumours (NRCT), this thesis investigates time trends in cancer diagnosed under age 15 in residents of Britain during 1966-2005 (54650 cases), and considers potential sources of artefact in detail. Several different methods are used to estimate completeness of NRCT registration. The history of methods for diagnosis and registration of childhood cancers in Britain is described, and predictions are made for effects on recorded incidence. For each of the 12 main diagnostic groups, Poisson regression is used to fit continuous time trends and ‘step’ models to the annual age-sex-standardised rates by year of birth and year of diagnosis. Age-specific rates by period, and quinquennial standardised rates for diagnostic subgroups, are shown graphically. For three broad groups (leukaemia, CNS tumours and other cancer), geographical variation is compared by period of diagnosis. The results of these analyses are discussed in relation to the predicted artefacts. The evidence for a positive association between affluence and recorded incidence of childhood leukaemia is briefly reviewed. A special form of diagnostic artefact, the ‘fatal infection’ hypothesis, is proposed as an explanation of both this association and the leukaemia time trend. This hypothesis is examined in a novel test based on clinical data. The recorded incidence of childhood cancer in Britain increased in each of 12 diagnostic groups during 1966-2005 (from 0.5% per year for bone cancer to 2.5% for hepatic cancer, with 0.7% for leukaemia). Evidence presented here suggests that these increases are probably artefacts of diagnosis and registration. The potential implications for epidemiological studies of childhood cancer should be considered.

SOCIAL WORK / MA(SS) (SOCIAL WORK)

Cervical cancer is reported to be the first among the five leading cancers affecting women in South Africa and the leading cancer among Black females. There is a high incidence and mortality rate among underserved and under-screened women presenting with cervical cancer as they are often unable to access screening facilities. Very little has been done to establish the factors in private health care provision that influence the implementation of cervical screening programmes. The study investigates factors influencing cervical cancer screening programme implementation among private medical practitioners in Soshanguve to establish whether private practitioners assume their roles in cervical screening. The intention is to provide guidelines for a screening programme that could be implemented in private health facilities. The study is qualitative, explorative, descriptive and contextual, using interviews and observation as the main data-collection methods. Lack of awareness and interest, failure to inform patients about cervical screening, age and gender of medical practitioners were among the factors identified. The study concluded that awareness programmes about cervical screening and materials containing information on cervical cancer and predisposing factors should be designed. The utilization of cheaper laboratory service providers could increase participation by making the test affordable and accessible to cash patients. The establishment of a private Pap clinic within medical practices or even as an independent entity is also recommended. / Health Studies / M.A. (Public Health)