No AccessJournal of UrologyAdult Urology1 Dec 2018

Multiparametric Magnetic Resonance Imaging Features Identify Aggressive Prostate Cancer at the Phenotypic and Transcriptomic Level

    View All Author Information


    Multiparametric magnetic resonance imaging is a diagnostic tool for prostate cancer with limited data on prognostic use. We sought to determine whether multiparametric magnetic resonance could predict aggressive prostate cancer features.

    Materials and Methods:

    We retrospectively analyzed the records of 206 patients who underwent radical prostatectomy between 2013 and 2017. All patients had available RNA expression data on the final pathology specimen obtained from a location corresponding to a lesion location on multiparametric magnetic resonance imaging. The association between the PIRADS™ (Prostate Imaging Reporting and Data System) score and adverse pathology features were analyzed. We also performed differential transcriptomic analysis between the PIRADS groups. Factors associated with adverse pathology were analyzed using a multivariable logistic regression model.


    Lesion size (p = 0.03), PIRADS score (p = 0.02) and extraprostatic extension (p = 0.01) associated significantly with the Decipher® score. Multivariable analysis showed that the PIRADS score (referent PIRADS 3, OR 8.1, 95% CI 1.2–57.5, p = 0.04), the Gleason Grade Group (referent 3, OR 5.6, 95% CI 1.5–21.1, p = 0.01) and prostate specific antigen (OR 1.103, 95% CI 1.011–1.203) were risk factors for adverse pathology findings. The difference between PIRADS 4 and 5 did not reach significance (OR 1.9, 95% CI 0.8–4.5, p = 0.12). However, the PI3K-AKT-mTOR, WNT-β and E2F signaling pathways were more active in PIRADS 5 than in PIRADS 4 cases.


    The PIRADS score is associated with adverse pathology results, increased metastatic risk and differential genomic pathway activation.


    • 1 : PI-RADS Prostate Imaging-Reporting and Data System: 2015, version 2. Eur Urol2016; 69: 16. Google Scholar
    • 2 : Assessment of PI-RADS v2 for the detection of prostate cancer. Eur J Radiol2016; 85: 726. Google Scholar
    • 3 : Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA2015; 313: 390. Google Scholar
    • 4 : Prostate MRI prior to radical prostatectomy: effects on nerve sparing and pathological margin status. Res Rep Urol2017; 9: 55. Google Scholar
    • 5 : Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer. Radiology2011; 259: 453. Google Scholar
    • 6 : Genomic predictors of outcome in prostate cancer. Eur Urol2015; 68: 1033. Google Scholar
    • 7 : Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One2013; 8: e66855. Google Scholar
    • 8 : Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat Genet2015; 47: 736. Google Scholar
    • 9 : The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: definition of grading patterns and proposal for a new grading system. Am J Surg Pathol2016; 40: 244. Google Scholar
    • 10 : PI-RADS™ Prostate Imaging-Reporting and Data System, version 2. Reston: American College of Radiology2015. Google Scholar
    • 11 : Decipher correlation patterns post prostatectomy: initial experience from 2 342 prospective patients. Prostate Cancer Prostatic Dis2016; 19: 374. Google Scholar
    • 12 : Exon array data analysis using Affymetrix power tools and R statistical software. Brief Bioinform2011; 12: 634. Google Scholar
    • 13 : A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics2012; 100: 337. Google Scholar
    • 14 : A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol2014; 66: 550. Google Scholar
    • 15 : Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. JAMA1998; 280: 969. Google Scholar
    • 16 : Tissue-based genomics augments post-prostatectomy risk stratification in a natural history cohort of intermediate- and high-risk men. Eur Urol2016; 69: 157. Google Scholar
    • 17 : Tissue-specific synthesis and oxidative metabolism of estrogens. J Natl Cancer Inst Monogr2000; 27: 95. Google Scholar
    • 18 : Estrogens as endogenous genotoxic agents—DNA adducts and mutations. J Natl Cancer Inst Monogr2000; 27: 75. Google Scholar
    • 19 : Clinical implications of PTEN loss in prostate cancer. Nat Rev Urol2018; 15: 222. Google Scholar
    • 20 : Wnt/beta-catenin signalling in prostate cancer. Nat Rev Urol2012; 9: 418. Google Scholar
    • 21 : Nuclear mTOR acts as a transcriptional integrator of the androgen signaling pathway in prostate cancer. Genes Dev2017; 31: 1228. Google Scholar
    • 22 : WNT signalling in prostate cancer. Nat Rev Urol2017; 14: 683. Google Scholar
    • 23 : Role of PI3K-AKT-mTOR and Wnt signaling pathways in transition of G1-S phase of cell cycle in cancer cells. Front Oncol2013; 3: 85. Google Scholar
    • 24 : E2F transcription factors control the roller coaster ride of cell cycle gene expression. Methods Mol Biol2016; 1342: 71. Google Scholar
    • 25 : Molecular alterations in prostate cancer and association with MRI features. Prostate Cancer Prostatic Dis2017; 20: 430. Google Scholar
    • 26 : Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget2016; 7: 53362. Google Scholar
    • 27 : PI-RADS version 2 for prediction of pathological downgrading after radical prostatectomy: a preliminary study in patients with biopsy-proven Gleason score 7 (3+4) prostate cancer. Eur Radiol2016; 26: 3580. Google Scholar