Histologic analysis of tissue samples is often a critical step in the diagnosis of disease. However, this type of assessment is inherently subjective, and consequently a high degree of variability may occur between results produced by different pathologists. Histologic analysis is also a very time-consuming task for pathologists. Computer-based quantitative analysis of tissue samples shows promise for both reducing the subjectivity of traditional manual tissue assessments, as well as potentially reducing the time required to analyze each sample. <p>The objective of this thesis project was to investigate image processing techniques and to develop software which could be used as a diagnostic aid in pathology assessments of cattle lung tissue samples. The software examines digital images of tissue samples, identifying and highlighting the presence of a set of features that indicate disease, and that can be used to distinguish various pulmonary diseases from one another. The output of the software is a series of segmented images with relevant disease indicators highlighted, and measurements quantifying the occurrence of these features within the tissue samples. Results of the software analysis of a set of 50 cattle lung tissue samples were compared to the detailed manual analysis of these samples by a pathology expert.<p>The combination of image analysis techniques implemented in the thesis software shows potential. Detection of each of the disease indicators is successful to some extent, and in some cases the analysis results are extremely good. There is a large difference in accuracy rates for identification of the set of disease indicators, however, with sensitivity values ranging from a high of 94.8% to a low of 22.6%. This wide variation in result scores is partially due to limitations of the methodology used to determine accuracy.

- / -

Lung cancer is the leading cause of cancer related death worldwide; despite recent treatment developments survival rates remain poor and are closely related to the patient’s clinical stage. Even among patients with early-stage lung cancer, which is amenable to surgical resection, prognosis is highly variable; some go on to live disease-free for many years whereas others quickly recur. Although post-operative chemotherapy is available it has associated morbidities and it is unclear which patients would benefit; therefore, there is a need for more effective stratification of patients. The adenocarcinoma sub-type of lung cancer is known to be morphologically heterogeneous however the majority of observed growth patterns, assessed by light microscopy, can be characterised into one of five formations: lepidic, papillary, acinar, solid and micropapillary. The morphology of each tumour has been proposed as a marker of prognosis and several studies have published a link between the most prevalent growth pattern and prognosis; suggesting those with predominantly solid or micropapillary tumours to have the least favourable outcomes. Indeed, it is now recommended that the proportion of each growth pattern and the predominant growth pattern should be reported for all resected lung adenocarcinomas; although no differential treatments have been recommended based on this assessment. The aim of this study was to determine whether combining the analysis of clinicopathological; morphological; and candidate protein, molecular genetic and transcriptomic characteristics in a single cohort of 208 early-stage, resected, adenocarcinomas with clinical follow-up could be used to identify a subset of patients at high risk of recurrence. Comprehensive morphological analysis was carried out including the presence, proportion and number of individual growth patterns; the predominant growth pattern as well as features previously associated with tumour grade (the presence of large numbers of mitotic figures, apoptotic bodies, inflammatory cells, prominent nucleoli, pleomorphic tumour cells, dyscohesive tumour cells and large amounts of necrosis and scar tissue within the tumour). In addition, gene expression was assessed using a panel of 31 cell-cycle related genes, EGFR and KRAS mutation status was determined, and EGFR and TTF1 protein expression investigated. In this study the predominant growth pattern defined by histopathology showed no ability to identify a group of patients with a poorer prognosis either in univariable or multivariable analysis. Univariable analysis identified nodal status [hazard ratio of N1 compared to N0 was 2.16 (95% CI 1.48 to 3.16, p< 0.0005)], clinical stage [hazard ratios of stage IIa and IIb compared to stage Ia were 3.15 (95% CI 1.73 to 5.73, p< 0.0005) and 2.22 (95% CI 1.10 to 4.48, p= 0.025) respectively], the presence of a significant amount of the papillary growth pattern [the hazard ratio of those with less than 8.5% papillary pattern was 0.657 (95% CI 0.44 to 0.98, p= 0.035)], and overall tumour grade score (including an assessment of necrosis, mitosis, apoptosis, nucleoli, scar tissue and inflammatory cells) [hazard ratio 1.71 (95% CI 1.14 to 2.56, p= 0.008)] as significantly associated with prognosis. Multivariable analysis using Cox’s proportional hazards model identified clinical stage (p< 0.0005), the presence of a significant amount of the papillary growth pattern (p= 0.048) and the presence of large numbers of mitotic figures (p=0.029) and apoptotic bodies (p= 0.015) as independently associated with disease specific survival; although after correction for type I errors only clinical stage remained significantly associated with prognosis with patients with stage Ia disease having a significantly better outcomes [hazard ratio 0.418 (95% CI 0.20 to 0.86)]. Classification and regression tree analysis (CART) was used to further explore the data and to develop decision trees for the prognostication of early-stage lung adenocarcinoma patients. Receiver operating characteristic analysis based on 5- year survival showed a minimal improvement in the area under the curve between a model utilizing currently available clinicopathologic characteristics only [nodal status and lesion size, (area under the curve 0.704, 95% CI 0.631 to 0.777)] and one including growth pattern characteristics [area under the curve 0.725, 95% CI 0.654 to 0.796]. The greatest improvement in prognostic accuracy was observed when gene expression analysis was included in the analysis [area under the curve 0.749, 95% CI 0.673 to 0.825]; however even this showed very little impact compared to routinely used clinicopathologic variables. This analysis suggests that the recommended characterisation of lung adenocarcinoma histology is not a robust predictor of patient outcomes; even a broader model which also included indicators of tumour grade and molecular characteristics was unable to identify a model sufficiently robust to implement into clinical practice and thereby potentially alter patient treatment. Currently routinely collected clinical characteristics; including nodal status, size and clinical stage; continue to provide the most robust method of prognostication and detailed and time-consuming morphological analysis offers no significant benefit to the patient.

Made available in DSpace on 2014-06-11T19:33:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-05-29Bitstream added on 2014-06-13T19:23:18Z : No. of bitstreams: 1 silva_mfp_dr_botfm.pdf: 1509111 bytes, checksum: 1fdb3e3719a4fe9608e3770a4a516b86 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Psoríase é doença inflamatória da derme e epiderme. Avaliação clínica é parâmetro essencial na indicação e avaliação de resultados terapêuticos. Alteração clínica é tradução macromorfológica de achados micromorfológicos. Investigar a correlação entre índices de gravidade clínica e histopatologia, antes e depois de tratamento sistêmico de pacientes com psoríase em placa. Avaliação clínica padronizada: PASI; “PASI” de região topográfica da lesão alvo; índice de inflamação total e índice de inflamação da região topográfica“, expurgados da extensão comprometida e, correlação histopatológica. Nos momentos pré e pós-tratamento sistêmico. Os escores clínicos e histopatológicos foram capazes de detectar melhora significativa entre o pré e pós-tratamento. Não houve correlação, pré-tratamento, entre os escores de gravidades dos índices clínicos utilizados e os escores histopatológicos. No pós-tratamento houve correlação entre índice de “inflamação total” e “inflamação da região topográfica da lesão alvo” e achados histopatológicos nos pacientes com PASI <12. Não houve correlação entre índices clínicos e histopatológicos em nenhum momento nos pacientes com PASI ≥ 12. Os índices clínicos utilizados e a análise histopatológica captaram a intensidade de melhoria clínica dos pacientes. Porém, em pacientes com doença moderada e grave os índices de gravidade clínica não se correlacionaram com as alterações histopatológicas. Palavras-chave: histopatologia, PASI, psoríase, tratamento. / Psoriasis is a chronic inflammatory disease of the skin. Clinical appraisal is essential to decide the treatment indication and results evaluation. Clinical aspects are the macro morphologic consequence of the histopathological changes. To investigate the correlation between clinical severity index and histopathological alterations observed before and after systemic treatment plaque psoriasis patients. Structured clinical evaluation of psoriasis area and severity index (PASI), local “PASI”, “total inflammatory index” and “inflammatory index of a target lesion”, these ones without compromised area, and correlated with histopathological changes, both, before and after treatment. The clinical and histopathological scores were able of detecting significant improvement after treatment. There were no agreement among all clinical indexes and the histopathological appraisal. Considering the post-treatment evaluation, correlation was observed among “total inflammatory index” and “inflammatory index of a target lesion” and histopathological scores from patients with PASI < 12.There was no correlation between clinical indexes and histopathological scores in any moment considering patients with PASI ≥12. The clinical indexes and the histopathological analysis were able to detect the degree of patients’ improvement. However, among patients with moderated and severe disease the clinical indexes showed no correlation with the observed histopathological changes.

Orientadores: Maria Leticia Cintra, Maria Elvira Pizzigatti Correa / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciencias Medicas / Made available in DSpace on 2018-08-11T18:34:10Z (GMT). No. of bitstreams: 1 Soares_TaniaCristinaBenetti_M.pdf: 14432539 bytes, checksum: c0ee9e9bfd4554afbcf1633cfa93fc09 (MD5) Previous issue date: 2008 / Resumo: O estabelecimento de critérios mínimos para o diagnóstico da doença do enxerto contra o hospedeiro crônica (DECHc), identificando aqueles com significância prognostica, bem como a padronização deste achados, pode adicionar subsídios para a conduta terapêutica, facilitar o trabalho de pesquisadores e aperfeiçoar a comunicação entre eles. O objetivo deste trabalho foi estudar dois sistemas de classificação histológica para DECHc em glândulas salivares menores (GSM), um proposto por Horn et al. em 1995 que tem sido empregado no Departamento de Anatomia Patológica, UNICAMP, e outro proposto por Shulman et al. em 2006. Este foi um trabalho retrospectivo que avaliou biópsias de GSM coletadas no momento do diagnóstico clínico de DECHc oral. As amostras de GSM foram obtidas de pacientes tratados por transplante de medula óssea (TMO) convencional, com enxerto HLA- idêntico de doadores aparentados entre 1994 e 2006. Do total de 154 pacientes com DECHc oral, 65 amostras de GSM de 65 pacientes foram selecionadas. A mediana de idade dos pacientes foi de 36 (12 - 59 anos), sendo 39 homens e 26 mulheres. As doenças de base foram LMC (n = 37), LMA (n = 11) LLA (n = 8), AA (n = 6), HPN (n = 1) e SMD (n = 2). As fontes de células tronco para os TMOs foram sangue periférico (n = 28) e medula óssea (n = 37). As biópsias foram avaliadas segundo os sistemas de classificação de Horn et al. e Shulman et al. de forma cega e independente por duas observadoras, e o resultado de consenso foi considerado para análise estatística. Foi encontrada uma significante correlação entre os diagnósticos histológicos propostos por Horn et al. e Shulman et al. (R = 87%, p = 0,0001). O critério "linfócitos periductais com exocitose nos ductos", da classificação de Shulman et al., mostrou correlação com a sobrevida global do paciente (p = 0,007). Assim, a migração de linfócitos através do epitélio ductal pode exercer influência negativa na evolução clínica dos pacientes. "Infiltrado linfocítico periductal" no sistema de graduação de Horn et al. Mostrou correlação significante com a forma clínica (localizada ou extensa) da DECHc (p = 0,04). Os resultados deste estudo apontam para a importância prognostica adversa dos critérios "infiltrado linfocítico periductal" e "linfócitos periductais com exocitose nos ductos" e podem auxiliar no entendimento da DECHc oral. / Abstract: Establishing minimum criteria for cGVHD diagnosis, identifying those with prognostic significance, as well as standardizing these features, may add subsidies to conduct therapy, to facilitate the work of researchers and to improve communication between them. The goal of this work was to study two systems for cGVHD histological classification in minor salivary glands (MSG), one proposed by Horn in 1995 that has been used at the Pathology Department, UNICAMP, and another one proposed by Shulman et al., in 2006. It was a retrospective study including MSG biopsies collected at the diagnosis of oral cGVHD. The MSG samples were obtained from patients treated by conventional HSCT, from HLA matched- sibling donors between 1994 - 2006. Among 154 patients with oral cGVHD, 65 samples were selected from 65 patients. Patients median age was 36 (12 - 59 years), 39 males and 26 females. The underlying diseases were CML (n = 37), AML (n = 11), ALL (n = 8), SAA (n = 6), PNH (n = 1) and MDS (n = 2). The sources of HSCT used were bone marrow (n = 37) and peripheral blood (n = 28). The histopathological specimens were blindly and independently examined by two observers and consensus results were considered for statistical processing. A significant correlation was found between the histological grades proposed by Horn and Shulman (R = 87%, p = 0.0001). The criterium "Periductal lymphocytic infiltrate with exocytosis into duct", in Shulman's classification, was correlated with global survival (p = 0,007). "Peri-ductal lymphocyte infiltrate" in Horn's system showed significant correlation with the clinical cGVHD form (localized or extensive) of cGVHD (p = 0.04). The results suggested similarity between final diagnoses obtained either by Horn or Shulman's classification. The periductal lymphocytic infiltrate was the most important histological criterium for clinical form of cGVHD. The lymphocytes migration through ductal epithelium might exert negative influence in patient outcome. The findings point to the adverse prognostic significance of the following criteria: "periductal lymphocytic infiltrate" and "peri-ductal lymphocytic infiltrate with exocytosis into duct" and may improve the understanding of oral cGVHD. / Mestrado / Ciencias Biomedicas / Mestre em Ciências Médicas

Orientadores: Carlos Takahiro Chone, Eulalia Sakano / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-23T21:36:35Z (GMT). No. of bitstreams: 1 Dias_ArethusaIngriddeLizMedeiros_M.pdf: 1854166 bytes, checksum: ba03fb9925b6895d98d898626cbb0986 (MD5) Previous issue date: 2013 / Resumo: Existem poucos estudos que correlacionam os aspectos histopatológicos dos pólipos nasossinusais após tratamento com corticosteróide nasal. O objetivo do presente estudo foi analisar as alterações histopatológicas dos pólipos nasais antes e após o uso de corticosteróide tópico nasal por três meses. Foi utilizado ensaio clínico, sem grupo controle. Biópsias de pólipos nasais de pacientes com polipose nasossinusal foram realizadas antes e após três meses de uso de budesonida 50 mcg, duas vezes ao dia, para análise histopatológica. Nos 35 pacientes incluídos, observou-se, após corticoterapia, um aumento da intensidade do edema na submucosa, sendo que 50% destes apresentavam edema intenso, uma redução de 47% de pacientes com infiltrado intenso de eosinófilos (p= 0,07) e não houve alteração quanto ao infiltrado de linfócitos, neutrófilos e plasmócitos. Com relação à presença dos cistos glandulares, houve aumento estatisticamente significante (p=0,031). Não se observou variação na espessura da membrana basal (p=0,344). Concluiu-se que o uso de corticosteróide tópico nasal por três meses mostrou variações no estroma e na intensidade das células dos pólipos nasossinusais / Abstract: There are few papers regarding the effect of corticosteroids on histopathology of sinonasal polyps. The aim of this study was to analyze the histopathology of nasal polyps before and after three months of nasal topical corticosteroids. It was used a non-controlled clinical trial. Nasal polyps biopsies of patients with sinonasal polyposis were performed before and after three months of budesonide 50 mcg BID for histopathological analysis. Among the 35 patients included, after steroid therapy, it was observed an increase in submucosal edema intensity, half of them presenting intense edema, a 47% reduction of cases with intense eosinophils infiltration (p = 0.07). Lymphocytes, neutrophils and plasma cells infiltration in polyps was not altered by treatment. It was observed statistically significant increase in presence of glandular cysts after treatment (p = 0.031). The thickness of the basement membrane did not changes (p=0.344). This study concluded that three months use of topical nasal corticosteroids resulted in stromal and cellular variations of sinonasal polyps / Mestrado / Otorrinolaringologia / Mestra em Ciências Médicas

Histopathology of a murine lymph node, 9 days post infection with Coccidioides. Magnification 10X

Magister Scientiae (Biodiversity and Conservation Biology) - MSc (Biodiv and Cons Biol) / Ichthyophonus hoferi is a highly pathogenic histozoic parasite that has low host specificity capable of producing mass mortalities of epizootic proportions in marine commercial fish populations. Currently in Southern Africa, I. hoferi has been reported from flathead mullet (Mugil cephalus) from the Kowie lagoon and from multiple species on exhibit at the Two Oceans Aquarium. Since epizootiologists rely on accurate assessments of prevalence to establish patterns of morbidity and mortality within populations, using the most accurate diagnostic techniques for accurate assessments of infection is imperative. Currently, several diagnostic techniques have been employed to detect I. hoferi in infected fish hosts. These include macroscopic examination of tissues, microscopic examinations of wet-mount squash preparations of tissue, histological examination of tissue sections, in vitro culture of tissue explants, the polymerase chain reaction (PCR) using I. hoferi-specific primers and real-time quantitative PCR (qPCR) using I. hoferi-specific primers and a hydrolysis probe.

Introduction: The placenta is a complex, disk-shaped organ vital to a successful pregnancy and responsible for materno-fetal exchange of vital gases and biochemicals. Instances of compromised placental development or function – collectively termed placenta dysfunction - underlies the most common and devastating pregnancy complications observed in North America, including preeclampsia (PE) and fetal growth restriction (FGR). A comprehensive histopathology examination of the placenta following delivery can help clarify obstetrical disease etiology and progression and offers tremendous potential in the identification of patients at risk of recurrence in subsequent pregnancies, as well as patients at high risk of chronic diseases in later life. However, these types of examinations require a high degree of specialized training and are resource intensive, limiting their availability to tertiary care centers in large city centres. The development of machine learning algorithms tailored to placenta histopathology applications may allow for automation and/or standardization of this important clinical exam – expanding its appropriate usage and impact on the health of mothers and infants. The primary objective of the current project is to develop and pilot the use of machine learning models capable of placental disease classification using digital histopathology images of the placenta. Methods: 1) A systematic review was conducted to identify the current methods being applied to automate histopathology screening to inform experimental design for later components of the project. Of 230 peer-reviewed articles retrieved in the search, 18 articles met all inclusion criteria and were used to develop guidelines for best practices. 2) To facilitate machine learning model development on placenta histopathology samples, a villi segmentation algorithm was developed to aid with feature extraction by providing objective metrics to automatically quantify microscopic placenta images. The segmentation algorithm applied colour clustering and a tophat transform to delineate the boundaries between neighbouring villi. 3) As a proof-of-concept, 2 machine learning algorithms were tested to evaluated their ability to predict the clinical outcome of preeclampsia (PE) using placental histopathology specimens collected through the Research Centre for Women’s and Infant’s Health (RCWIH) BioBank. The sample set included digital images from 50 cases of early onset PE, 29 cases of late onset PE and 69 controls with matching gestational ages. All images were pre-processed using patch extraction, colour normalization, and image transformations. Features of interest were extracted using: a) villi segmentation algorithm; b) SIFT keypoint descriptors (textural features); c) integrated feature extraction (in the context of deep learning model development). Using the different methods of feature extraction, two different machine learning approaches were compared - Support Vector Machine (SVM) and Convolutional Neural Network (CNN, deep learning). To track model improvement during training, cross validation on 20% of the total dataset was used (deep learning algorithm only) and the trained algorithms were evaluated on a test dataset (20% of the original dataset previously unseen by the model). Results: From the systematic review, 5 key steps were found to be essential for machine learning model development on histopathology images (image acquisition and preparation, image preprocessing, feature extraction, pattern recognition and classification model training, and model testing) and recommendations were provided for the optimal methods for each of the 5 steps. The segmentation algorithm was able to correctly identify individual villi with an F1 score of 80.76% - a significantly better performance than recently published methods. A maximum accuracy of 73% for the machine learning experiments was obtained when using textural features (SIFT keypoint descriptors) in an SVM model, using onset of PE disease (early vs. late) as the output classification of interest. Conclusion: Three major outcomes came of this project: 1) the range of methods available to develop automated screening tools for histopathology images with machine learning were consolidated and a set of best practices were proposed to guide future projects, 2) a villi segmentation tool was developed that can automatically segment all individual villi from an image and extract biologically relevant features that can be used in machine learning model development, and 3) a prototype machine learning classification tool for placenta histopathology was developed that was able to achieve moderate classification accuracy when distinguishing cases of early onset PE and late onset PE cases from controls. The collective body of work has made significant contributions to the fields of placenta pathology and computer vision, laying the foundation for significant progress aimed at integrating machine learning tools into the clinical setting of perinatal pathology.

Indiana University-Purdue University Indianapolis (IUPUI) / Meningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science.