Statistical Analysis of Prostate Functional Magnetic Resonance Imaging Data

Detalhes bibliográficos
Autor(a) principal: Parreira, Liliana Anacleto
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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spelling 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
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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