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Statistical techniques for addressing the clinico-radiological paradox in multiple sclerosis / Jordan D. Dworkin.
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Dworkin, Jordan D., author.
- Language:
- English
- Subjects (All):
- Biostatistics.
- Medical imaging.
- Epidemiology.
- Epidemiology and Biostatistics--Penn dissertations.
- Penn dissertations--Epidemiology and Biostatistics.
- Local Subjects:
- Biostatistics.
- Medical imaging.
- Epidemiology.
- Epidemiology and Biostatistics--Penn dissertations.
- Penn dissertations--Epidemiology and Biostatistics.
- Genre:
- Academic theses.
- Physical Description:
- 1 online resource (83 pages)
- Contained In:
- Dissertations Abstracts International 82-03B.
- Place of Publication:
- [Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2020.
- Language Note:
- English
- System Details:
- Mode of access: World Wide Web.
- text file
- Summary:
- Medical imaging technology has allowed for unparalleled insight into the structure and function of the human brain, giving clinicians powerful new tools for disease diagnosis and monitoring. Yet the complex and high-dimensional nature of imaging data makes computational analysis challenging. In multiple sclerosis (MS), this complexity is typically simplified by identifying regions of visible tissue damage and measuring spatial extent. However, many common radiological measures have been shown to be only weakly associated with clinical outcomes (a discovery that has been referred to as "the clinico-radiological paradox"). We attempt to bridge this gap by developing statistical methods capable of extracting clinically relevant information from MRI scans in MS. Here, we discuss three such techniques: a texture modeling approach to improve research on lesion dynamics; a biomarker detection algorithm to support diagnostic decision-making; and a flexible multi-modal group differences test to facilitate exploration of subtle disease processes. The performance of these methods is illustrated using simulated and real data, and the opportunities and obstacles for their clinical use are discussed.
- Notes:
- Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
- Advisors: Shinohara, Russell T.; Committee members: Kristin Linn; Yong Chen; Amit Bar-Or.
- Department: Epidemiology and Biostatistics.
- Ph.D. University of Pennsylvania 2020.
- Local Notes:
- School code: 0175
- ISBN:
- 9798672161808
- Access Restriction:
- Restricted for use by site license.
- This item must not be sold to any third party vendors.
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