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Predicting Tumour Response

Overview
Journal Cancer Imaging
Publisher Springer Nature
Specialties Oncology
Radiology
Date 2013 Sep 25
PMID 24061161
Citations 3
Authors
Affiliations
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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.

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References
1.
Choi H . Critical issues in response evaluation on computed tomography: lessons from the gastrointestinal stromal tumor model. Curr Oncol Rep. 2005; 7(4):307-11. DOI: 10.1007/s11912-005-0055-4. View

2.
Bossard C, Kury S, Jamet P, Senellart H, Airaud F, Ramee J . Delineation of the infrequent mosaicism of KRAS mutational status in metastatic colorectal adenocarcinomas. J Clin Pathol. 2012; 65(5):466-9. DOI: 10.1136/jclinpath-2011-200608. View

3.
Bomanji J, Papathanasiou N . ¹¹¹In-DTPA⁰-octreotide (Octreoscan), ¹³¹I-MIBG and other agents for radionuclide therapy of NETs. Eur J Nucl Med Mol Imaging. 2012; 39 Suppl 1:S113-25. DOI: 10.1007/s00259-011-2013-8. View

4.
Hutchings M, Loft A, Hansen M, Moller Pedersen L, Buhl T, Jurlander J . FDG-PET after two cycles of chemotherapy predicts treatment failure and progression-free survival in Hodgkin lymphoma. Blood. 2005; 107(1):52-9. DOI: 10.1182/blood-2005-06-2252. View

5.
Juweid M, Wiseman G, Vose J, Ritchie J, Menda Y, Wooldridge J . Response assessment of aggressive non-Hodgkin's lymphoma by integrated International Workshop Criteria and fluorine-18-fluorodeoxyglucose positron emission tomography. J Clin Oncol. 2005; 23(21):4652-61. DOI: 10.1200/JCO.2005.01.891. View