The mechanisms that control growth processes in biology tissues have attracted continuous research interest despite their complexity. With the emergence of big data experimental approaches there is an urgent need to develop statistical and computational models to fit the experimental data and that can be used to make predictions to guide future research. In this work we apply statistical methods on growth process of different biological tissues, focusing on development of neuron dendrites and tumor cells. We first examine the neuron cell growth process, which has implications in neural tissue regenerations, by using a computational model with uniform branching probability and a maximum overall length constraint. One crucial outcome is that we can relate the parameter fits from our model to real data from our experimental collaborators, in order to examine the usefulness of our model under different biological conditions. Our methods can now directly compare branching probabilities of different experimental conditions and provide confidence intervals for these population-level measures. In addition, we have obtained analytical results that show that the underlying probability distribution for this process follows a geometrical progression increase at nearby distances and an approximately geometrical series decrease for far away regions, which can be used to estimate the spatial location of the maximum of the probability distribution. This result is important, since we would expect maximum number of dendrites in this region; this estimate is related to the probability of success for finding a neural target at that distance during a blind search. We then examined tumor growth processes which have similar evolutional evolution in the sense that they have an initial rapid growth that eventually becomes limited by the resource constraint. For the tumor cells evolution, we found an exponential growth model best describes the experimental data, based on the accuracy and robustness of models. Furthermore, we incorporated this growth rate model into logistic regression models that predict the growth rate of each patient with biomarkers; this formulation can be very useful for clinical trials. Overall, this study aimed to assess the molecular and clinic pathological determinants of breast cancer (BC) growth rate in vivo.

The objective of this study is to address some important questions associated with stomach cancer patients using the data from the Surveillance Epidemiology and End Results (SEER) program of the United States. To better understand the behavior of stomach cancer, we first perform parametric analysis for each patient group (white male, white female, African American male, African American female, other male and female) to identify the probability distribution function which can best characterize the behavior of the malignant stomach tumor sizes. We evaluate the effects of patients’ age, gender and race on the malignant stomach tumor sizes by developing quantile regression models, which gives us a better understanding of the behavior of the malignant stomach tumors. We also proposed statistical models with respect to patients’ malignant stomach tumor size as a function of age for different races and gender group, respectively. The proposed models were evaluated to attest their prediction quality. Furthermore, we have identified the rate of change of the malignant tumor size as a function of age, for gender and race. We evaluated the routine treatment of stomach cancer using parametric and nonparametric survival analysis. We have found that stomach cancer patients who receive surgery with radiation together have a better survival probability than the patients who receive only radiation. We performed decision tree analysis to assist the physician in recommending to his patients the most effective treatment that is a function of their characteristics.

PURPOSE: The purpose of this study was to assess the ability of Micro Computed Tomography (Micro CT) to measure primary tumor size in breast lumpectomy specimens, as compared to the histopathological measurement. METHODS: This was a diagnostic study involving women who were scheduled to have breast lumpectomy surgery at the Massachusetts General Hospital (MGH) Department of surgery from June 2011 - September 2011. Those who met the study eligibility criteria were recruited to participate in the study. The study was approved by the MGH Institutional Review Board (IRB). All the participants provided consent prior to their participation in the study. The lumpectomy specimens of 45 subjects were scanned by Micro CT scan for no longer than 15 minutes, they were then delivered to the gross pathology lab for processing via the standard pathological protocol. Later on, the maximum dimension of the invasive breast tumor was obtained from the Micro CT image and was compared to the corresponding pathology report for each subject. RESULTS: We found that Micro CT tends to overestimate the breast malignant tumor size. However, there were few differences in T-stage classification between Micro CT and pathology. Overall, Micro CT demonstrated good agreement with pathological tumor size and staging. For Invasive ductal carcinoma, Micro CT showed a substantial agreement with pathological tumor size and staging. However, Micro CT showed no agreement with pathological tumor size and staging for invasive lobular carcinoma. CONCLUSIONS: Micro CT is a promising modality in measuring and staging the invasive ductal carcinoma.

Objectives: To investigate the controversially discussed relationship between tumor size and the occurrence of primary synchronous metastatic disease in renal cell cancer (RCC). Patients and Methods: A consecutive RCC cohort of 2,058 patients (150 primary metastatic) who underwent surgery between 1995 and 2010 was investigated. Rates of synchronous metastases were calculated for stratified groups of tumor size. Uni- and multivariate logistic regression models were calculated for the correlation of tumor size with primary metastatic disease. Results: The rate of metastatic disease increased with increasing tumor size. Tumor size was significantly correlated with synchronous metastatic disease (p < 0.001, c-index 0.772), but for RCCs ≤ 4 cm in size no significant correlation was found. Regarding tumors ≤ 5 cm in size, the correlation became significant (p = 0.028, c-index 0.621). A multivariate logistic regression model for the prediction of synchronous metastatic disease including tumor size, age and comorbidity yielded a significant c-index of 0.82 and was used to construct a nomogram. Conclusion: Our data confirm the correlation between tumor size and the rate of synchronous metastatic disease. Small renal tumors <4 cm in size have a low risk of synchronous metastatic disease. The risk becomes significantly associated with tumor size for tumors ≤ 5 cm.

The mechanisms that control growth processes in biology tissues have attracted continuous research interest despite their complexity. With the emergence of big data experimental approaches there is an urgent need to develop statistical and computational models to fit the experimental data and that can be used to make predictions to guide future research. In this work we apply statistical methods on growth process of different biological tissues, focusing on development of neuron dendrites and tumor cells. We first examine the neuron cell growth process, which has implications in neural tissue regenerations, by using a computational model with uniform branching probability and a maximum overall length constraint. One crucial outcome is that we can relate the parameter fits from our model to real data from our experimental collaborators, in order to examine the usefulness of our model under different biological conditions. Our methods can now directly compare branching probabilities of different experimental conditions and provide confidence intervals for these population-level measures. In addition, we have obtained analytical results that show that the underlying probability distribution for this process follows a geometrical progression increase at nearby distances and an approximately geometrical series decrease for far away regions, which can be used to estimate the spatial location of the maximum of the probability distribution. This result is important, since we would expect maximum number of dendrites in this region; this estimate is related to the probability of success for finding a neural target at that distance during a blind search. We then examined tumor growth processes which have similar evolutional evolution in the sense that they have an initial rapid growth that eventually becomes limited by the resource constraint. For the tumor cells evolution, we found an exponential growth model best describes the experimental data, based on the accuracy and robustness of models. Furthermore, we incorporated this growth rate model into logistic regression models that predict the growth rate of each patient with biomarkers; this formulation can be very useful for clinical trials. Overall, this study aimed to assess the molecular and clinic pathological determinants of breast cancer (BC) growth rate in vivo.

Submitted by Ubirajara Cruz (ubirajara.cruz@gmail.com) on 2018-06-08T16:49:44Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) dissertacao_andressa_rossato.pdf: 1728672 bytes, checksum: 5c211a94a7b35ba4201cdb05f7928c41 (MD5) / Approved for entry into archive by Aline Batista (alinehb.ufpel@gmail.com) on 2018-06-11T16:56:18Z (GMT) No. of bitstreams: 2 dissertacao_andressa_rossato.pdf: 1728672 bytes, checksum: 5c211a94a7b35ba4201cdb05f7928c41 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-06-11T16:56:18Z (GMT). No. of bitstreams: 2 dissertacao_andressa_rossato.pdf: 1728672 bytes, checksum: 5c211a94a7b35ba4201cdb05f7928c41 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-12-12 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Esta dissertação aborda neoplasmas da glândula mamária de cadelas. Os dados são relacionados aos casos diagnosticados no Serviço de Oncologia Veterinário (SOVET) e no Laboratório Regional de Diagnóstico (LRD), da Universidade Federal de Pelotas (UFPEL), nos anos de 2010 a 2015. Com base nesses achados foram elaborado três artigos científicos. No primeiro artigo foi realizado uma abordagem para um melhor entendimento clínico, patológico, sobre diagnóstico e tratamento de neoplasmas mamários em caninos, pois com o aumento da expectativa de vida dos animais de companhia e, consequentemente o aumento da casuística clínica veterinária de animais com neoplasmas mamários se fez necessário essa abordagem literária. No segundo artigo cientifico foi realizado um levantamento de amostras de neoplasmas mamários recebidas nos anos de 2010 a 2015 no SOVET e LRD. Assim foi caracterizado dados epidemiológicos e anatomopatológicos relacionados as cadelas e aos neoplasmas mamários e associado com o tempo de sobrevida de cadelas com carcinossarcomas e carcinomas mamários. Foi demonstrado que os carcinossarcomas são neoplasmas de maior malignidade por apresentarem piores graus histológicos e maior comprometimento de margens cirúrgicas, bem como determinaram um menor tempo de sobrevida dos animais, com este tipo histológico bem como quando associados com outros carcinomas, não tendo predileção por raça, acometendo animais idosos e que utilizavam terapia hormonal. No terceiro artigo científico foi realizado uma avaliação histopatológica dos linfonodos de cadelas que apresentaram metástases de neoplasmas mamários e correlacionados estes com seu tipo histológico. O grupo dos carcinossarcomas, tipos especiais e sarcomas apresentaram o maior índice de metástases em linfonodos, com a frequência maior em animais sem raça definida (SRD), com uma média de 10 anos de idade. / In this dissertation, neoplasms of the mammary gland of bitches are discussed. The data are related to the cases diagnosed at the Veterinary Oncology Service (SOVET) and at the Regional Diagnostic Laboratory (LRD), Federal University of Pelotas (UFPEL), from 2010 to 2015. Based on these findings A literature review and two scientific papers were prepared. In the review of the literature, an approach for a better clinical and pathological understanding of the diagnosis and treatment of canine mammary neoplasms was carried out, since the increase in the life expectancy of companion animals and, consequently, the increase in the veterinary clinical casuistry of animals with Neoplasms if this literary approach was necessary. In the first scientific article was carried out a survey of samples of breast neoplasms received in the years 2010 to 2015 in SOVET and LRD. Thus epidemiological and anatomopathological data related to bitches and mammary neoplasms were associated with the survival time of bitches with carcinosarcomas and mammary carcinomas. Carcinosarcomas have been shown to be neoplasms of higher malignancy because they present worse histological grades and greater compromise of surgical margins, as well as they have determined a shorter survival time of the animals with this histological type as well as when associated with other carcinomas, not having a predilection for race, Affecting elderly animals and using hormonal therapy. In the second scientific article, a histopathological evaluation of the lymph nodes of bitches who presented metastasis of mammary neoplasms and correlated with their histological type was performed. The group of carcinosarcomas, special types and sarcomas presented the highest index of lymph node metastases, with the highest frequency of non-defined animals (SRD), with an average of 10 years of age.