Predicting tumour response

Kyle, Samuel D., Law, W. Phillip and Miles, K. A. (2013) Predicting tumour response. Cancer Imaging, 13 3: 381-390. doi:10.1102/1470-7330.2013.9039

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Author Kyle, Samuel D.
Law, W. Phillip
Miles, K. A.
Title Predicting tumour response
Journal name Cancer Imaging   Check publisher's open access policy
ISSN 1740-5025
Publication date 2013-01-01
Year available 2013
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1102/1470-7330.2013.9039
Open Access Status DOI
Volume 13
Issue 3
Start page 381
End page 390
Total pages 10
Place of publication Beckenham, Kent, United Kingdom
Publisher e-MED
Language eng
Subject 2730 Oncology
2741 Radiology Nuclear Medicine and imaging
3614 Radiological and Ultrasound Technology
Abstract Response prediction is an important emerging concept in oncologic imaging, with tailored, individualized treatment regimens increasingly becoming the standard of care. This review aims to define tumour response and illustrate the ways in which imaging techniques can demonstrate tumour biological characteristics that provide information on the likely benefit to be received by treatment. Two imaging approaches are described: identification of therapeutic targets and depiction of the treatment-resistant phenotype. The former approach is exemplified by the use of radionuclide imaging to confirm target expression before radionuclide therapy but with angiogenesis imaging and imaging correlates for genetic response predictors also demonstrating potential utility. Techniques to assess the treatment-resistant phenotype include demonstration of hypoperfusion with dynamic contrast-enhanced computed tomography and magnetic resonance imaging (MRI), depiction of necrosis with diffusion-weighted MRI, imaging of hypoxia and tumour adaption to hypoxia, and 99mTc-MIBI imaging of P-glycoprotein mediated drug resistance. To date, introduction of these techniques into clinical practice has often been constrained by inadequate cross-validation of predictive criteria and lack of verification against appropriate response end points such as survival. With further refinement, imaging predictors of response could play an important role in oncology, contributing to individualization of therapy based on the specific tumour phenotype. This ability to predict tumour response will have implications for improving efficacy of treatment, cost-effectiveness and omission of futile therapy.
Keyword Biomarker
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: Official 2014 Collection
School of Medicine Publications
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