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dc.contributor.authorJensen, Line Rørstadnb_NO
dc.date.accessioned2014-12-19T14:23:36Z
dc.date.available2014-12-19T14:23:36Z
dc.date.created2010-04-23nb_NO
dc.date.issued2010nb_NO
dc.identifier311788nb_NO
dc.identifier.isbn978-82-471-2012-5 (printed ver.)nb_NO
dc.identifier.isbn978-82-471-2013-2 (electronic ver.)nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/264695
dc.description.abstractBreast cancer patients with locally advanced disease are often treated with chemotherapy prior to surgery, to downstage primary inoperable disease. There is a large variation in treatment response between patients, and methods that can predict or early detect treatment e_ects are needed to optimize therapy individually. Magnetic resonance (MR) has evolved as an important diagnostic tool in oncology, both for detection and follow_up in the course of treatment. Several MR methods are often used during one examination, providing both detailed anatomical images and functional images of perfusion, water diffusion or metabolic information. In dynamic contrast enhanced (DCE) MRI a series of images are acquired before and after intravenous injection of a contrast agent, demonstrating an increase in signal intensity of highly perfused tissues as in tumors. The formation of new blood vessels is an important step in tumor progression, and aggressive tumors are often highly vascularized. Thus DCE-MRI has potential for in vivo imaging of tumor blood supply, and functional changes may be assessed. Diffusion weighted (DW) MRI exploits the natural diffusion of water molecules, and may be used to study tumor properties at a cellular level. The motion of water molecules in tissue is restricted by structures such as cell membranes and macromolecules, and DW-MRI may be used to detect changes in e.g. cell density. Metabolic information can be studied both with in vivo MR spectroscopy (MRS) before the tumor is resected, and with ex vivo MRS of tumor biopsies. MR signals from small metabolites as choline compounds are used to study tumor metabolism, and may give important information on treatment response mechanisms. The aim of this work was to use a combination of MR methods to assess treatment response both in pre_clinical human tumor models in mice and in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). Both in vivo MR protocols and methods for analysis of the data were established during this work. Overall, differences in tumor vascularity were detected with DCE-MRI, both in breast cancer xenografts treated with chemotherapy and in colon cancer xenografts influenced by the fatty acid TTA. Changes in response to chemotherapy were also found with DCE-MRI in breast cancer patients, in addition to a correlation between parameters after DCE-MRI before treatment with overall survival. The apparent diffusion coeffcient derived from DW-MRI in breast cancer patients appeared to increase after one cycle of chemotherapy, as was also found in a study of breast cancer xenografts. In addition, treated breast tumor models had lower levels of choline compounds when compared to controls, as measured with MRS. Overall, the work in this thesis presents a useful set of complementary MR methods, well suited for treatment monitoringnb_NO
dc.languageengnb_NO
dc.publisherNTNUnb_NO
dc.relation.ispartofseriesDoktoravhandlinger ved NTNU, 1503-8181; 2010:29nb_NO
dc.relation.ispartofseriesDissertations at the Faculty of Medicine, 0805-7680; 426nb_NO
dc.relation.haspartJensen, LR; Garzon, B; Heldahl, MG; Bathen, TF; Goa, PE; Lundgren, S; Gribbestad, IS. Diffusion weighted and dynamic contrast enhanced MRI in evaluationof early treatment effects during neoadjuvant chemotherapy in breast cancer patients. .nb_NO
dc.titleEvaluation of treatment effects in cancer by MR imaging and spectroscopynb_NO
dc.typeDoctoral thesisnb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Det medisinske fakultet, Institutt for sirkulasjon og bildediagnostikknb_NO
dc.description.degreePhD i medisinsk teknologinb_NO
dc.description.degreePhD in Medical Technologyen_GB


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