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An integrated companion diagnostics assay for predicting biochemical recurrence following radical prostatectomy

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TitleInfo
Title
An integrated companion diagnostics assay for predicting biochemical recurrence following radical prostatectomy
Name (type = personal)
NamePart (type = family)
Lee
NamePart (type = given)
George C.
DisplayForm
George Lee
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Madabhushi
NamePart (type = given)
Anant
DisplayForm
Anant Madabhushi
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Pierce
NamePart (type = given)
Mark
DisplayForm
Mark Pierce
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Ganesan
NamePart (type = given)
Shridar
DisplayForm
Shridar Ganesan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Tomaszewski
NamePart (type = given)
John E
DisplayForm
John E Tomaszewski
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = personal)
NamePart (type = family)
Master
NamePart (type = given)
Stephen R
DisplayForm
Stephen R Master
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-01
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
The most common treatment of prostate cancer (CaP) is via radical prostatectomy (RP), of which 75,000 are performed in the United States each year. However, within the current paradigm, 15-40% of RP treatments ultimately fail in the form of biochemical recurrence (BCR) within 5 years. Gleason scoring, derived from visual inspection of tissue morphology, has been the gold standard for distinguishing aggressive CaP for over 40 years. Furthermore, the current initiative towards personalized health care has attempted to utilize an integrated predictor via molecular markers such as prostate specific c antigen (PSA) to identify men with aggressive localized CaP. However, the non-specificity of these tests has led to an over-treatment of CaP, which is responsible for increased morbidity that is both stressful and costly for the patient. This dissertation attempts to develop the algorithms that could pave the way for a new class of integrated predictors, which can combine histomorphometric and molecular features into an integrated biomarker and present the information needed for better patient care. Our overall goal was to predict BCR in CaP patients following RP treatment. A host of novel machine learning tools were developed to create integrated diagnostic tests, including dimensionality reduction (Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS)) and data integration (Supervised Multi-view Canonical Correlation Analysis (sMVCCA)) methodologies to handle complex, non-linear, high dimensional and heterogeneous biomedical data. Furthermore, the development and discovery of unique discriminatory features for differentiating aggressive CaP were necessary for the understanding of cancer progression and the foundation of an integrated biomarker. Novel histomorphometric features (Co-occurring Gland Tensors (CGTs) and Cell Orientation Entropy (COrE)) were developed to quantify important differentiating image-based characteristics of CaP morphology. These methods were shown to outperform Kattan nomogram and Gleason scoring for predicting BCR following RP. Lastly, fusion of histomorphometry and protein expression into an integrated signature was performed via sMVCCA, and demonstrated improved identification of men with BCR following RP compared to histomorphometric and proteomic signatures alone.
Subject (authority = RUETD)
Topic
Biomedical Engineering
Subject (authority = ETD-LCSH)
Topic
Prostatectomy
Subject (authority = ETD-LCSH)
Topic
Prostate-specific antigen
Subject (authority = ETD-LCSH)
Topic
Data integration (Computer science)
Subject (authority = ETD-LCSH)
Topic
Prostate--Cancer--Treatment
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5243
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xxiii, 138 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by George C. Lee
Subject (authority = ETD-LCSH)
Topic
Biochemical markers
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3XD0ZR2
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Lee
GivenName
George
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-12-22 21:25:49
AssociatedEntity
Name
George Lee
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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