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Title page for ETD etd-07152013-120602


Type of Document Dissertation
Author Whisenant, Jennifer Gray
URN etd-07152013-120602
Title Assessing Treatment Response of HER2+ Breast Cancer Xenografts with PET and MRI
Degree PhD
Department Chemical and Physical Biology
Advisory Committee
Advisor Name Title
Thomas E. Yankeelov Committee Chair
John C. Gore Committee Co-Chair
H. Charles Manning Committee Member
J. Oliver McIntyre Committee Member
Todd E. Peterson Committee Member
Keywords
  • medical imaging
  • assessing treatment response
  • positron emission tomography
  • magnetic resonance imaging
  • HER2+ xenografts
  • breast cancer
Date of Defense 2013-06-24
Availability unrestricted
Abstract
With the development of anti-cancer therapies that specifically target the tumor and its microenvironment, the current radiographic analysis of treatment response that uses one-dimensional changes in tumor size may not be the most sensitive (or accurate) approach. Functional and molecular imaging techniques have a fundamental role in oncology as they possess the ability to noninvasively measure treatment-induced changes in specific vascular, cellular, and molecular characteristics of the tumor. While much progress has been made to improve the quality of information obtained from these imaging techniques, obstacles remain. In particular, a lack of standardized imaging protocols, inadequate understanding of whether changes in imaging biomarkers predict clinical outcomes related to therapy, and lack of validation to assist in the interpretation of imaging data have limited clinical translation of these techniques into routine patient care. This dissertation sought to unravel some of those issues by optimizing experimental protocols for several small-animal PET and MRI techniques, assess protocol reproducibility, and evaluate the ability of several PET and MRI techniques to assess early treatment response in HER2+ breast cancer models exposed to trastuzumab. Additionally, imaging parameters were compared to histological correlates to better understand the relationship between in vivo data and the underlying biology.
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