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Automated image-based detection and grading of lymphocytic infiltration in breast cancer histopathology
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Automated image-based detection and grading of lymphocytic infiltration in breast cancer histopathology
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ETD_2279
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052094
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eng
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theses
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Topic
Biomedical Engineering
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(authority = ETD-LCSH)
Topic
Breast--Cancer--Diagnosis
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Diagnostic imaging
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Lymphocytes
Abstract
The identification of phenotypic changes in breast cancer (BC) histopathology is of significant clinical importance in predicting disease outcome and prescribing appropriate therapy. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with a variety of prognoses and theragnoses (i.e. response to treatment) in BC patients. In this thesis work a computer-aided diagnosis (CADx) system is detailed for quantitatively measuring the extent of LI from hematoxylin and eosin (H & E) stained histopathology. The CADx system is subsequently applied to BC patients expressing the HER2 gene (HER2+ BC), where LI extent has been found to correlate with nodal metastasis and distant recurrence. Although LI may be graded qualitatively by BC pathologists, there is currently no quantitative and reproducible method for measuring LI extent in HER2+ BC histopathology. Hence, a CADx system that performs this task will potentially help clinicians predict disease outcome and allow them to make better therapy recommendations for HER2+ BC patients. The CADx methodology comprises three key steps. First, a combination of region-growing and Markov Random Field algorithms is used to detect individual lymphocyte nuclei and isolate areas of LI in digitized H & E stained histopathology images. The centers of individual detected lymphocytes are used as vertices to construct a series of graphs (Voronoi Diagram, Delaunay Triangulation, and Minimum Spanning Tree) and a total of 50 architectural features describing the spatial arrangement of lymphocytes are extracted from each image. By using Graph Embedding, a non-linear dimensionality reduction method, to project the high-dimensional feature vectors into a reduced 3D embedding space, it is possible to visualize the underlying manifold that represents the continuous nature of the LI phenotype. Over a set of 100 randomized cross-validation trials, a Support Vector Machine classifier shows that the architectural feature set distinguishes HER2+ BC histopathology samples containing high and low levels of LI with a classification accuracy greater than 90%.
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electronic resource
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xii, 39 p. : ill.
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M.S.
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Includes bibliographical references (p. 35-35)
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by Ajay Basavanhally
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Basavanhally
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Ajay
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1985-
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Ajay Basavanhally
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Madabhushi
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chair
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Advisory Committee
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Anant Madabhushi
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(type = family)
Cai
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Li
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internal member
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Advisory Committee
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Li Cai
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Ganesan
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Shridar
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Advisory Committee
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Shridar Ganesan
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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2010
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2010-01
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3ZK5GT3
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ETD graduate
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Notice
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Open
Reason
Permission or license
Note
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Basavanhally
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Ajay
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DateTime
2009-12-08 14:56:24
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Ajay Basavanhally
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Rutgers University. Graduate School - New Brunswick
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Author Agreement License
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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.
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