Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/53809 |
Resumo: | Trabalho de projeto de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2022 |
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Statistical Analysis of Prostate Functional Magnetic Resonance Imaging DataCancro da próstataressonância magnéticaextensão extracapsularbiomarcadoresregressão logísticaTrabalhos de projeto de mestrado - 2022Departamento de Estatística e Investigação OperacionalTrabalho de projeto de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2022Prostate cancer is the most commonly diagnosed cancer in males, being one of the main causes of cancer related death in men. Management decisions for patients with prostate cancer are complicated and present a dilemma for both patients and their clinicians as prostate cancers demonstrate a wide range of biologic activity with the majority of cases not leading to a prostate cancer-specific death. Furthermore, the current treatment options for men with localized prostate cancer are aggressive and have significant side effects, such as incontinence, rectal injury and impotence. Thus it is clear that the challenges to the medical research community are to accurately predict a given prostate cancer’s behavior to, select those patients who require therapy and to treat the cancer with the appropriate level of intensity while preserving the patient’s quality of life. The treatment for this type of cancer that is considered the most effective is radical prostatectomy, which consists of surgical removal of the prostate and seminal vesicles. The enormous technological development in the area of magnetic resonance imaging (MRI) with the arising of new equipment and methods of image acquisition and processing more and more sophisticated, has led to an increasing use of this technique in areas such as diagnosis support, tumour staging and therapeutic decision. The excellent spatial resolution and the diversity of contrasts used in MRI (for example, anatomical image and diffusion) give this technique a high sensitivity and specificity in the detection of prostate tumours, being fundamental for the planning of minimally invasive robotic surgical interventions. The interest in mapping non-invasive histological/functional/anatomical features using MRI as a possible alternative to the pathological anatomy and predictive biomarker in prostate surgeries has recently increased. However, such methodologies need clinical validation. In this study, we will be questioning whether the MRI is an effective method for studying prostate cancer before operating, thus staging the prostate tumour through biomarkers, and then comparing the tumour stage attribution before the operation with the actual tumour stage found after operating the patient. The predictive factor for the presence of extracapsular disease in patients with prostatic neoplasia will also be studied. A logistic regression will be performed to identify which factors are significantly associated with the probability of occurrence of extracapsular disease, that is, a logistic regression model will be used to predict the staging of the tumour, based on the observed characteristics of the patient. Data variables included age, prostate-specific antigen (PSA), tumor length contact (TLC), tumor volume, Gleason score.Nunes, Maria Helena Mouriño Silva, 1969-Repositório da Universidade de LisboaParreira, Liliana Anacleto202220212024-12-27T00:00:00Z2022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/53809enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:00:00Zoai:repositorio.ul.pt:10451/53809Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:04:48.266050Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
title |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
spellingShingle |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data Parreira, Liliana Anacleto Cancro da próstata ressonância magnética extensão extracapsular biomarcadores regressão logística Trabalhos de projeto de mestrado - 2022 Departamento de Estatística e Investigação Operacional |
title_short |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
title_full |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
title_fullStr |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
title_full_unstemmed |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
title_sort |
Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data |
author |
Parreira, Liliana Anacleto |
author_facet |
Parreira, Liliana Anacleto |
author_role |
author |
dc.contributor.none.fl_str_mv |
Nunes, Maria Helena Mouriño Silva, 1969- Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Parreira, Liliana Anacleto |
dc.subject.por.fl_str_mv |
Cancro da próstata ressonância magnética extensão extracapsular biomarcadores regressão logística Trabalhos de projeto de mestrado - 2022 Departamento de Estatística e Investigação Operacional |
topic |
Cancro da próstata ressonância magnética extensão extracapsular biomarcadores regressão logística Trabalhos de projeto de mestrado - 2022 Departamento de Estatística e Investigação Operacional |
description |
Trabalho de projeto de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2022 |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022 2022-01-01T00:00:00Z 2024-12-27T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/53809 |
url |
http://hdl.handle.net/10451/53809 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
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embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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