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Changes in Structural Networks after...

Yeh, Joy.

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Changes in Structural Networks after Radiation Therapy in Patients with Brain Tumors.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Changes in Structural Networks after Radiation Therapy in Patients with Brain Tumors./
Author:	Yeh, Joy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	31 p.
Notes:	Source: Masters Abstracts International, Volume: 56-01.
Contained By:	Masters Abstracts International56-01(E).
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10165384
ISBN:	9781369197693

Changes in Structural Networks after Radiation Therapy in Patients with Brain Tumors.
Yeh, Joy.

Changes in Structural Networks after Radiation Therapy in Patients with Brain Tumors. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 31 p.

Source: Masters Abstracts International, Volume: 56-01.

Thesis (M.S.)--University of California, San Francisco, 2016.

Introduction - Radiation therapy (RT) received by brain tumor patients after tumor resection can affect brain connectivity in various degrees. Structural connectivity changes in brain matter can be modeled by diffusion MRI and tractography between areas of the brain that act as nodes of the network. We hypothesize that graph theory measures, including global network descriptors and local node characteristics, may change with the application of radiation therapy and evolve over time.

ISBN: 9781369197693Subjects--Topical Terms:

3172799
Medical imaging.

Changes in Structural Networks after Radiation Therapy in Patients with Brain Tumors.
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020 $a 9781369197693
035 $a (MiAaPQ)AAI10165384
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100 1 $a Yeh, Joy. $3 3286103
245 1 0 $a Changes in Structural Networks after Radiation Therapy in Patients with Brain Tumors.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 31 p.
500 $a Source: Masters Abstracts International, Volume: 56-01.
500 $a Adviser: Duan Xu.
502 $a Thesis (M.S.)--University of California, San Francisco, 2016.
520 $a Introduction - Radiation therapy (RT) received by brain tumor patients after tumor resection can affect brain connectivity in various degrees. Structural connectivity changes in brain matter can be modeled by diffusion MRI and tractography between areas of the brain that act as nodes of the network. We hypothesize that graph theory measures, including global network descriptors and local node characteristics, may change with the application of radiation therapy and evolve over time.
520 $a Methods - Six patients with brain tumor resections were imaged either before or immediately following RT and then again six months later. Diffusion data were registered with a brain atlas (AAL), and post-gad T1- and T2-weighted FLAIR images were clinically marked for tumor-affected tissues. Networks were constructed using 90 AAL regions as nodes and connecting streamlines as edges, weighted by the average fractional anisotropy (FA) within those streamlines. Four global and four local network measures were examined. Tumor-affected node sets were defined in each patient and examined for changes, then compared against contralateral nodes and all non-affected nodes. Obtained network measures were correlated with cognitive data.
520 $a Results - Network average clustering coefficient increased significantly between scans (p=0.0313). Local clustering, local efficiency, and node strength showed increasing trends in selected node sets. Node strength increased significantly with T1 and T2 image-based definitions of tumor-affected nodes six months post-RT in a single patient. Correlation was detected between cognitive scores and betweenness centrality in select tumor-affected nodes.
520 $a Conclusions - Several connectivity measures appeared to be altered in patients after radiation therapy in a span of six months, including increases in network average clustering. Small sample size limited the level of confidence of observed changes in local measures and correlation to cognitive scores but suggests connectivity changes in tumor-affected regions and betweenness centrality in relation to cognitive scores as points of interests in future studies.
590 $a School code: 0034.
650 4 $a Medical imaging. $3 3172799
650 4 $a Medicine. $3 641104
650 4 $a Neurosciences. $3 588700
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710 2 $a University of California, San Francisco. $b Biomedical Imaging. $3 2106351
773 0 $t Masters Abstracts International $g 56-01(E).
790 $a 0034
791 $a M.S.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10165384