In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI

Prostate Cancer Prostatic Dis. 2016 Jun;19(2):168-73. doi: 10.1038/pcan.2015.61. Epub 2016 Jan 12.

Abstract

Background: Magnetic resonance imaging (MRI) is emerging as a robust, noninvasive method for detecting and characterizing prostate cancer (PCa), but limitations remain in its ability to distinguish cancerous from non-cancerous tissue. We evaluated the performance of a novel MRI technique, restriction spectrum imaging (RSI-MRI), to quantitatively detect and grade PCa compared with current standard-of-care MRI.

Methods: In a retrospective evaluation of 33 patients with biopsy-proven PCa who underwent RSI-MRI and standard MRI before radical prostatectomy, receiver-operating characteristic (ROC) curves were performed for RSI-MRI and each quantitative MRI term, with area under the ROC curve (AUC) used to compare each term's ability to differentiate between PCa and normal prostate. Spearman rank-order correlations were performed to assess each term's ability to predict PCa grade in the radical prostatectomy specimens.

Results: RSI-MRI demonstrated superior differentiation of PCa from normal tissue, with AUC of 0.94 and 0.85 for RSI-MRI and conventional diffusion MRI, respectively (P=0.04). RSI-MRI also demonstrated superior performance in predicting PCa aggressiveness, with Spearman rank-order correlation coefficients of 0.53 (P=0.002) and -0.42 (P=0.01) for RSI-MRI and conventional diffusion MRI, respectively, with tumor grade.

Conclusions: RSI-MRI significantly improves upon current noninvasive PCa imaging and may potentially enhance its diagnosis and characterization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Neoplasm Grading
  • Neoplasm Staging
  • Prostatic Neoplasms / diagnostic imaging*
  • Prostatic Neoplasms / pathology*
  • Prostatic Neoplasms / surgery
  • ROC Curve
  • Retrospective Studies
  • Tumor Burden