Paracoccidioidomycosis in patients with Lymphoma and review of published literature

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Applying enhancement filters in the pre-processing of images of lymphoma

Lymphoma is a type of cancer that affects the immune system, and is classified as Hodgkin or non-Hodgkin. It is one of the ten types of cancer that are the most common on earth. Among all malignant neoplasms diagnosed in the world, lymphoma ranges from three to four percent of them. Our work presents a study of some filters devoted to enhancing images of lymphoma at the pre-processing step. Here the enhancement is useful for removing noise from the digital images. We have analysed the noise caused by different sources like room vibration, scraps and defocusing, and in the following classes of lymphoma: follicular, mantle cell and B-cell chronic lymphocytic leukemia. The filters Gaussian, Median and Mean-Shift were applied to different colour models (RGB, Lab and HSV). Afterwards, we performed a quantitative analysis of the images by means of the Structural Similarity Index. This was done in order to evaluate the similarity between the images. In all cases we have obtained a certainty of at least 75%, which rises to 99% if one considers only HSV. Namely, we have concluded that HSV is an important choice of colour model at pre-processing histological images of lymphoma, because in this case the resulting image will get the best enhancement.

Classification of histological images based on the stationary wavelet transform

Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.

Metabolic activity of the sticker's lymphosarcoma

1. 1. Some parameters (glycolysis, respiration, levels of glycolytic enzymes) of the lymphoid cells from the Sticker's lymphosarcoma were established in order to better define the biochemical behavior of the venereal tumor of the dog. 2. 2. For comparative purposes lymphocytes from peripheral blood of normal tumor-bearing dogs were also studied. 3. 3. Lactic acid produced by the tumor cells during aerobic glycolysis is liberated in the reaction medium. 4. 4. Oxygen uptake is enhanced in the presence of succinate, but not with pyruvate, α-ketoglutarate, or malate as substrates. 5. 5. Higher levels of some of the enzymes from the glycolytic pathways as well as differences on the physicochemical and kinetic properties of the glycolytic regulatory enzymes are found in Sticker's tumor cells, when compared with the lymphocytes from peripheral blood of normal and tumor-bearing dogs. 6. 6. A fructose-bisphosphate positively modulated pyruvatekinase is found in the tumor cells. © 1987.

Sarcoma histiocítico: análise molecular pela técnica de hibridação genômica comparativa, microRNA e imunoistoquímica

Pós-graduação em Patologia - FMB