Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models

Tumor acidosis is an important biomarker in cancer. We have developed a noninvasive imaging method, termed acidosis Chemical Exchange Saturation Transfer (acidoCEST) MRI to measure extracellular pH (pHe) in the tumor microenvironment. Chapter 1 introduces the importance of measuring tumor acidosis a...

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Main Author: Chen, Liu Qi
Other Authors: Pagel, Mark D.
Language:en_US
Published: The University of Arizona. 2014
Subjects:
MRI
Online Access:http://hdl.handle.net/10150/323450
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spelling ndltd-arizona.edu-oai-arizona.openrepository.com-10150-3234502015-10-23T05:35:12Z Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models Chen, Liu Qi Pagel, Mark D. Pagel, Mark D. Aspinwall, Craig Bandarian, Vahe Trouard, Theodore CEST MRI preclinical imaging Chemistry acidoCEST Tumor acidosis is an important biomarker in cancer. We have developed a noninvasive imaging method, termed acidosis Chemical Exchange Saturation Transfer (acidoCEST) MRI to measure extracellular pH (pHe) in the tumor microenvironment. Chapter 1 introduces the importance of measuring tumor acidosis and presents various imaging modalities and their shortcoming to measure pHe. Chapter 2 describes the optimization of acidoCEST MRI for in vivo pHe measurement. The acidoCEST MRI protocol consists of a CEST-FISP acquisition and Lorentzian line shape fittings. We determined the optimal saturation time, saturation power and bandwidth, 5 sec, 2.8 µT and 90 Hz respectively. We also tried various routes of administration to increase contrast agent uptake in the tumor. We decided upon 200 µL bolus followed by 150 µL/hr infusion. The optimized acidoCEST MRI protocol was tested on a mammary carcinoma mouse model of MDA- MB-231. Our method can detect an increase in pHe in the bladder and tumor of the mice treated with bicarbonate. We used this optimized acidoCEST MRI method to measure pHe in lymphoma tumor model of Raji, Ramos and Granta 519 as described in Chapter 3. Pixel-wise pHe maps showed tumor heterogeneity. The pHe of Raji, Ramos and Granta 519 were determined to be mildly acidic with no significant difference. Chapter 4 describes the evolution of pixel-wise analysis in more detail. Besides the pHe map and spatial heterogeneity, we were able to determine the % contrast agent uptake. We monitored these biomarkers in two different mammary carcinoma mouse models, MDA- MB-231 and MCF-7 longitudinally and made comparisons between the different tumor models: MCF-7 were more acidic, more heterogeneous and faster growing than MDA- MB-231. 2014 text Electronic Dissertation http://hdl.handle.net/10150/323450 en_US Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. The University of Arizona.
collection NDLTD
language en_US
sources NDLTD
topic CEST
MRI
preclinical imaging
Chemistry
acidoCEST
spellingShingle CEST
MRI
preclinical imaging
Chemistry
acidoCEST
Chen, Liu Qi
Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models
description Tumor acidosis is an important biomarker in cancer. We have developed a noninvasive imaging method, termed acidosis Chemical Exchange Saturation Transfer (acidoCEST) MRI to measure extracellular pH (pHe) in the tumor microenvironment. Chapter 1 introduces the importance of measuring tumor acidosis and presents various imaging modalities and their shortcoming to measure pHe. Chapter 2 describes the optimization of acidoCEST MRI for in vivo pHe measurement. The acidoCEST MRI protocol consists of a CEST-FISP acquisition and Lorentzian line shape fittings. We determined the optimal saturation time, saturation power and bandwidth, 5 sec, 2.8 µT and 90 Hz respectively. We also tried various routes of administration to increase contrast agent uptake in the tumor. We decided upon 200 µL bolus followed by 150 µL/hr infusion. The optimized acidoCEST MRI protocol was tested on a mammary carcinoma mouse model of MDA- MB-231. Our method can detect an increase in pHe in the bladder and tumor of the mice treated with bicarbonate. We used this optimized acidoCEST MRI method to measure pHe in lymphoma tumor model of Raji, Ramos and Granta 519 as described in Chapter 3. Pixel-wise pHe maps showed tumor heterogeneity. The pHe of Raji, Ramos and Granta 519 were determined to be mildly acidic with no significant difference. Chapter 4 describes the evolution of pixel-wise analysis in more detail. Besides the pHe map and spatial heterogeneity, we were able to determine the % contrast agent uptake. We monitored these biomarkers in two different mammary carcinoma mouse models, MDA- MB-231 and MCF-7 longitudinally and made comparisons between the different tumor models: MCF-7 were more acidic, more heterogeneous and faster growing than MDA- MB-231.
author2 Pagel, Mark D.
author_facet Pagel, Mark D.
Chen, Liu Qi
author Chen, Liu Qi
author_sort Chen, Liu Qi
title Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models
title_short Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models
title_full Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models
title_fullStr Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models
title_full_unstemmed Development and Application of AcidoCEST MRI for Evaluating Tumor Acidosis in Pre-Clinical Cancer Models
title_sort development and application of acidocest mri for evaluating tumor acidosis in pre-clinical cancer models
publisher The University of Arizona.
publishDate 2014
url http://hdl.handle.net/10150/323450
work_keys_str_mv AT chenliuqi developmentandapplicationofacidocestmriforevaluatingtumoracidosisinpreclinicalcancermodels
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