Newer Imaging Modalities to Assess Tumor in the Prostate
Open Access
- 1 September 2004
- journal article
- review article
- Published by SAGE Publications in Cancer Control
- Vol. 11 (6), 353-357
- https://doi.org/10.1177/107327480401100602
Abstract
Background: Several advances in the imaging of prostate cancer have been made in recent years. Diagnostic staging has become increasingly complex and confusing as newer technologies have developed more rapidly than research has been able to confirm or refute the accuracy of these technologies. By the time research has been performed, the technology used for a study has often become outdated and newer and more sophisticated imaging has become available. Methods: We reviewed the literature on local and nodal staging of prostate cancer, as well as the role of magnetic resonance imaging (MRI), magnetic resonance spectroscopic imaging (MRSI), dynamic contrast-enhanced MRI, positron emission tomography (PET), endorectal power Doppler, lymphotropic MRI contrast agents, and future possibilities such as diffusion MRI. This review is not systematic, but rather focused on these imaging modalities. Results: Advances in MRI, ultrasound, and lymphotropic contrast agents have improved our ability to differentiate between T2 and T3 prostate tumors. PET imaging has proven less successful at staging prostate cancer. A literature review suggests patients with moderate risk of extracapsular extension benefit most from endorectal MRI evaluation. Spectroscopy, dynamic imaging, and lymphotropic contrast agents are expected to continue to improve sensitivity and specificity of staging of prostate cancer. Power Doppler evaluation with endorectal ultrasound has proved useful for evaluation during endorectal biopsy for identifying hypervascular tumors for directed biopsy. Diffusion-weighted MRI remains untested clinically and represents a future direction for research. Conclusions: Future studies using these new techniques are needed to demonstrate changes in outcomes in large patient populations.Keywords
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