Date Published:2014 Jan
It is now universally recognized that many prostate cancers are over-diagnosed and over-treated. The European Randomized Study of Screening for Prostate Cancer from 2009 evidenced that, to save one man from death from prostate cancer, over 1400 men need to be screened, and 48 need to undergo treatment. The detection of prostate cancer is traditionally based on digital rectal examination (DRE) and the measurement of serum prostate-specific antigen (PSA), followed by ultrasound-guided biopsy. The primary role of imaging for the detection and diagnosis of prostate cancer has been transrectal ultrasound (TRUS) guidance during biopsy. Traditionally, MRI has been used primarily for the staging of disease in men with biopsy-proven cancer. It has a well-established role in the detection of T3 disease, planning of radiation therapy, especially three-dimensional conformal or intensity-modulated external beam radiation therapy, and planning and guiding of interstitial seed implant or brachytherapy. New advances have now established that prostate MRI can accurately characterize focal lesions within the gland, an ability that has led to new opportunities for improved cancer detection and guidance for biopsy. Two new approaches to prostate biopsy are under investigation. Both use pre-biopsy MRI to define potential targets for sampling, and the biopsy is performed either with direct real-time MR guidance (in-bore) or MR fusion/registration with TRUS images (out-of-bore). In-bore and out-of-bore MRI-guided prostate biopsies have the advantage of using the MR target definition for the accurate localization and sampling of targets or suspicious lesions. The out-of-bore method uses combined MRI/TRUS with fusion software that provides target localization and increases the sampling accuracy of TRUS-guided biopsies by integrating prostate MRI information with TRUS. Newer parameters for each imaging modality, such as sonoelastography or shear wave elastography, contrast-enhanced ultrasound and MRI elastography, show promise to further enrich datasets.