Literature DB >> 20504274

Diffusion-weighted MRI for assessment of early cancer treatment response.

Stefanie Galbán1, Jean-Christophe Brisset, Alnawaz Rehemtulla, Thomas L Chenevert, Brian D Ross, Craig J Galbán.   

Abstract

Recent clinical practice for the management for cancer patients has begun to change from a statistical "one-size fits all" approach to medicine to more individualized care. Pre-treatment biomarkers (i.e. genetically and histologically based) have a growing role in providing guidance related to the appropriate therapy and likelihood of response; they do not take into account heterogeneity within the tumor mass. Thus, a biomarker which could be utilized to measure actual tumor response early following treatment initiation would provide an important opportunity to evaluate treatment effects on an individual patient basis. Diffusion weighted magnetic resonance imaging (DW-MRI) offers the opportunity to monitor treatment-associated alterations in tumor microenvironment using quantification of changes in tumor water diffusion values as a surrogate imaging biomarker. Results obtained thus far using DW-MRI have shown that changes in tumor diffusion values can be detected early following treatment initiation which correlate with traditional outcome measures. Sensitive imaging biomarkers are providing for the first time a means of assessing 3 dimensional tumor response early in the treatment cycle. This review highlights the development of DW-MRI and its proposed usefulness in the clinical management of cancer patients. The utility of DW-MRI for assessing therapeutic-induced response is further evaluated on tumors residing in the brain, head and neck and bone.

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Year:  2010        PMID: 20504274      PMCID: PMC4003912          DOI: 10.2174/138920110792246627

Source DB:  PubMed          Journal:  Curr Pharm Biotechnol        ISSN: 1389-2010            Impact factor:   2.837


  39 in total

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5.  Functional imaging in head and neck squamous cell carcinoma: correlation of PET/CT and diffusion-weighted imaging at 3 Tesla.

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10.  Quantification of heterogeneity observed in medical images.

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