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Added Value of Multiparametric Magnetic Resonance Imaging in Men Undergoing Radical Prostatectomy

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dc.date.accessioned 2020-06-01T10:35:10Z
dc.date.available 2020-06-01T10:35:10Z
dc.date.issued 2020-06-01
dc.identifier.uri http://hdl.handle.net/123456789/28981
dc.title Added Value of Multiparametric Magnetic Resonance Imaging in Men Undergoing Radical Prostatectomy en
ethesis.discipline none und
ethesis.department none und
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca
ethesis.university.URI http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97
ethesis.university Helsingin yliopisto fi
ethesis.university University of Helsinki en
ethesis.university Helsingfors universitet sv
dct.creator Pohjonen, Joona
dct.issued 2020
dct.language.ISO639-2 eng
dct.abstract Prediction of the pathological T-stage (pT) in men undergoing radical prostatectomy (RP) is crucial for disease management as curative treatment is most likely when prostate cancer (PCa) is organ-confined (OC). Although multiparametric magnetic resonance imaging (MRI) has been shown to predict pT findings and the risk of biochemical recurrence (BCR), none of the currently used nomograms allow the inclusion of MRI variables. This study aims to assess the possible added benefit of MRI when compared to the Memorial Sloan Kettering, Partin table and CAPRA nomograms and a model built from available preoperative clinical variables. Logistic regression is used to assess the added benefit of MRI in the prediction of non-OC disease and Kaplan-Meier survival curves and Cox proportional hazards in the prediction of BCR. For the prediction of non-OC disease, all models with the MRI variables had significantly higher discrimination and net benefit than the models without the MRI variables. For the prediction of BCR, MRI prediction of non-OC disease separated the high-risk group of all nomograms into two groups with significantly different survival curves but in the Cox proportional hazards models the variable was not significantly associated with BCR. Based on the results, it can be concluded that MRI does offer added value to predicting non-OC disease and BCR, although the results for BCR are not as clear as for non-OC disease. en
dct.subject Prostate cancer
dct.subject magnetic resonance imaging
dct.subject logistic regression
dct.subject survival analysis
dct.subject imputation
dct.language en
ethesis.isPublicationLicenseAccepted true
ethesis.language.URI http://data.hulib.helsinki.fi/id/languages/eng
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype.URI http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis
dct.identifier.ethesis E-thesisID:af50a075-6fab-40b9-89bc-42ab72674368
dct.identifier.urn URN:NBN:fi:hulib-202006012540
dc.type.dcmitype Text
ethesis.facultystudyline Systems Biology and Medicine fi
ethesis.facultystudyline Systems Biology and Medicine en
ethesis.facultystudyline Systems Biology and Medicine sv
ethesis.facultystudyline.URI http://data.hulib.helsinki.fi/id/SH50_136
ethesis.mastersdegreeprogram Life Science Informatics -maisteriohjelma fi
ethesis.mastersdegreeprogram Master's Programme in Life Science Informatics en
ethesis.mastersdegreeprogram Magisterprogrammet i Life Science Informatics sv
ethesis.mastersdegreeprogram.URI http://data.hulib.helsinki.fi/id/MH50_002

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