Advancements in MR imaging of the prostate: from diagnosis to interventions

Radiographics. 2011 May-Jun;31(3):677-703. doi: 10.1148/rg.313105139.

Abstract

Prostate cancer is the most frequently diagnosed cancer in males and the second leading cause of cancer-related death in men. Assessment of prostate cancer can be divided into detection, localization, and staging; accurate assessment is a prerequisite for optimal clinical management and therapy selection. Magnetic resonance (MR) imaging has been shown to be of particular help in localization and staging of prostate cancer. Traditional prostate MR imaging has been based on morphologic imaging with standard T1-weighted and T2-weighted sequences, which has limited accuracy. Recent advances include additional functional and physiologic MR imaging techniques (diffusion-weighted imaging, MR spectroscopy, and perfusion imaging), which allow extension of the obtainable information beyond anatomic assessment. Multiparametric MR imaging provides the highest accuracy in diagnosis and staging of prostate cancer. In addition, improvements in MR imaging hardware and software (3-T vs 1.5-T imaging) continue to improve spatial and temporal resolution and the signal-to-noise ratio of MR imaging examinations. Another recent advancement in the field is MR imaging guidance for targeted prostate biopsy, which is an alternative to the current standard of transrectal ultrasonography-guided systematic biopsy.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Biomarkers, Tumor / blood
  • Biopsy / methods
  • Disease Progression
  • Humans
  • Magnetic Resonance Imaging / methods*
  • Magnetic Resonance Imaging, Interventional
  • Male
  • Neoplasm Staging
  • Prognosis
  • Prostate-Specific Antigen / blood
  • Prostatic Neoplasms / diagnosis*
  • Prostatic Neoplasms / pathology
  • Prostatic Neoplasms / therapy
  • Ultrasonography, Interventional

Substances

  • Biomarkers, Tumor
  • Prostate-Specific Antigen