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Quantitative histomorphometry of digital pathology as a companion diagnostic

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TitleInfo
Title
Quantitative histomorphometry of digital pathology as a companion diagnostic
SubTitle
predicting outcome for ER+ breast cancers
Name (type = personal)
NamePart (type = family)
Basavanhally
NamePart (type = given)
Ajay Nagesh
NamePart (type = date)
1985-
DisplayForm
Ajay Basavanhally
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)
Ganesan
NamePart (type = given)
Shridar
DisplayForm
Shridar Ganesan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Cai
NamePart (type = given)
Li
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Li Cai
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
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)
Tomaszewski
NamePart (type = given)
John
DisplayForm
John Tomaszewski
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)
This work involves the creation of an image-based companion diagnostic framework that employs quantitative features extracted from whole-slide, H & E stained digital pathology (DP) images to distinguish patients based on disease outcome, with a clinical application aimed at distinguishing estrogen receptor-positive (ER+) breast cancer (BCa) patients with good and poor outcomes. Quantitative histomorphometry (QH) -- the conversion of a digitized histopathology slide into a series of quantitative measurements of tumor morphology -- is a rapidly growing field aimed at introducing advanced image analytics into the histopathological workflow. The thrust towards personalized medicine has led to the development of companion diagnostic tools that measure gene expression, yielding quantitative outcome predictions for improved disease stratification and customized therapies, e.g. Oncotype DX (Genomic Health, Inc.) for ER+ BCa. Yet, tumor morphology is often correlated with genomic assays, suggesting that genotypic variations in biologically distinct classes of tumors lead to distinct patterns of tumor cell morphology and tissue architecture in histopathology. The application of this work to ER+ BCa is highly relevant to current clinical needs. Current treatment guidelines recommend that the majority of women with ER+ BCa receive chemotherapy in addition to hormonal therapy; yet, approximately half will not benefit from chemotherapy while still enduring its harmful side effects. Hence, there is a clear need for the development of automated prognostic tools to identify women with poorer outcomes who will likely benefit from chemotherapy. The primary novel contributions of this work are (1) a color standardization system for improving the consistency in appearance of tissue structures across images, (2) the identification of tissue structures and corresponding QH signatures with prognostic value in ER+ BCa, (3) a multi-field-of-view framework for robust integration of prognostic information across whole-slide DP images, and (4) a method for predicting classifier performance for a large data cohort based on the availability of limited training data. This work will pave the way for the development of novel companion diagnostic systems capable of producing quantitative and reproducible image-based risk scores. These risk scores will play a vital role in decision support by helping clinicians predict patient outcome and prescribing appropriate therapies.
Subject (authority = RUETD)
Topic
Biomedical Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5275
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xxv, 123 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Ajay Nagesh Basavanhally
Subject (authority = ETD-LCSH)
Topic
Quantitative research
Subject (authority = ETD-LCSH)
Topic
Breast--Cancer--Research
Subject (authority = ETD-LCSH)
Topic
Breast--Cancer--Treatment
Subject (authority = ETD-LCSH)
Topic
Estrogen--Receptors
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/T3BC3WND
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
Basavanhally
GivenName
Ajay
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-01-02 15:13:13
AssociatedEntity
Name
Ajay Basavanhally
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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