Tumour response prediction by diffusion-weighted MR imaging: Ready for clinical use?
SourceCritical Reviews in Oncology Hematology, 83, 2, (2012), pp. 194-207
01 augustus 2012
Article / Letter to editor
Display more detailsDisplay less details
Critical Reviews in Oncology Hematology
SubjectNCMLS 2: Immune Regulation ONCOL 3: Translational research; ONCOL 3: Translational research; ONCOL 3: Translational research N4i 1: Pathogenesis and modulation of inflammation; ONCOL 3: Translational research NCMLS 4: Energy and redox metabolism; ONCOL 5: Aetiology, screening and detection
BACKGROUND: The efficacy of anticancer therapy is usually evaluated by anatomical imaging. However, this method may be suboptimal for the evaluation of novel treatment modalities, such as targeted therapy. Theoretically, functional assessment of tumour response by diffusion weighted imaging (DWI) is an attractive tool for this purpose and may allow an early prediction of response. The optimal use of this method has still to be determined. METHOD: We reviewed the published literature on clinical DWI in the prediction of response to anticancer therapy, especially targeted therapy. Studies investigating the role of DWI in patients with cancer either for response prediction and/or response monitoring were selected for this analysis. RESULTS: We identified 24 studies that met our criteria. Most studies showed a significant correlation between (changes in) apparent diffusion coefficient (ADC) values and treatment response. However, in different tumours and studies, both high and low pretreatment ADC were found to be associated with response rate. In the course of treatment, an increase in ADC was associated with response in most cases. CONCLUSION: The potential of DWI for (early) response monitoring of anticancer therapies has been demonstrated. However, validation is hampered by the lack of reproducibility and standardisation. We recommend that these issues should be properly addressed prior to further testing the clinical use of DWI in the assessment of treatments.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.