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Quantifying Uncertainties in Imaging...

Henscheid, Nicholas Patrick.

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Quantifying Uncertainties in Imaging-Based Precision Medicine.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantifying Uncertainties in Imaging-Based Precision Medicine./
Author:	Henscheid, Nicholas Patrick.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	273 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-11, Section: B.
Contained By:	Dissertations Abstracts International79-11B.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10811826
ISBN:	9780355915273

Quantifying Uncertainties in Imaging-Based Precision Medicine.
Henscheid, Nicholas Patrick.

Quantifying Uncertainties in Imaging-Based Precision Medicine. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 273 p.

Source: Dissertations Abstracts International, Volume: 79-11, Section: B.

Thesis (Ph.D.)--The University of Arizona, 2018.

This item must not be sold to any third party vendors.

In this work, we present a rigorous mathematical framework for the usage of multiple patient-specific molecular images to enable model-based precision medicine, a paradigm of medical decision making defined by the employment of mathematical models of treatment efficacy to direct optimized treatment decisions for individual patients. We address the question of how to define and compute patient-specific probability of treatment success, using random field theory to define the notion of in silico virtual patient ensembles and patient-specific virtual clinical trials. We then provide a novel and rigorous deterministic and statistical analysis of photon-processing Emission Computed Tomography (ECT) data, highlighting the importance of null functions and Poisson statistics in defining the virtual patient ensemble and probability of treatment success. We discuss novel high-performance parallel numerical methods to simulate virtual patient ensembles and photon processing ECT systems; these simulations will advance our understanding of the uncertainties inherent in imaging-based precision medicine. Finally, we present a spatially resolved model for chemotherapy efficacy that employs ECT data, and demonstrate how our framework can be used to define, compute and optimize patient-specific probability of treatment success in this setting.

ISBN: 9780355915273Subjects--Topical Terms:

1669109
Applied Mathematics.

Quantifying Uncertainties in Imaging-Based Precision Medicine.
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500 $a Source: Dissertations Abstracts International, Volume: 79-11, Section: B.
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520 $a In this work, we present a rigorous mathematical framework for the usage of multiple patient-specific molecular images to enable model-based precision medicine, a paradigm of medical decision making defined by the employment of mathematical models of treatment efficacy to direct optimized treatment decisions for individual patients. We address the question of how to define and compute patient-specific probability of treatment success, using random field theory to define the notion of in silico virtual patient ensembles and patient-specific virtual clinical trials. We then provide a novel and rigorous deterministic and statistical analysis of photon-processing Emission Computed Tomography (ECT) data, highlighting the importance of null functions and Poisson statistics in defining the virtual patient ensemble and probability of treatment success. We discuss novel high-performance parallel numerical methods to simulate virtual patient ensembles and photon processing ECT systems; these simulations will advance our understanding of the uncertainties inherent in imaging-based precision medicine. Finally, we present a spatially resolved model for chemotherapy efficacy that employs ECT data, and demonstrate how our framework can be used to define, compute and optimize patient-specific probability of treatment success in this setting.
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