European Radiology

, Volume 29, Issue 9, pp 4861–4870 | Cite as

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings

  • Andrei S. PuryskoEmail author
  • Cristina Magi-Galluzzi
  • Omar Y. Mian
  • Sarah Sittenfeld
  • Elai Davicioni
  • Marguerite du Plessis
  • Christine Buerki
  • Jennifer Bullen
  • Lin Li
  • Anant Madabhushi
  • Andrew Stephenson
  • Eric A. Klein



We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases).


This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions.


MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018).


MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI.

Key Points

• MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases.

• Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier.

• MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.


Prostatic neoplasms Magnetic resonance imaging Genes 



Grade group


Gleason score


Hematoxylin and eosin


Prostate cancer


Peripheral zone


Radical prostatectomy


Transition zone



We are grateful for the editorial assistance of Megan M. Griffiths, scientific writer for the Imaging Institute, Cleveland Clinic, Cleveland, OH.


This study has received funding from the Center for Clinical Genomics, Cleveland Clinic; Research Seed Grant Award Presented by Philips/Radiological Society of North America Research & Education Foundation; and GenomicDx Biosciences.

Compliance with ethical standards


The scientific guarantor of this publication is Andrei S. Purysko, MD.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Elai Davicioni, Christine Buerki, and Marguerite du Plessis are employees of GenomeDx Biosciences.

The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

The co-author Jennifer Bullen is a biostatistician of the Quantitative Health Sciences Department at Cleveland Clinic. She kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board due to the minimal risk and retrospective nature of this study.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2019_6114_MOESM1_ESM.docx (18 kb)
ESM 1 (DOCX 17 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Andrei S. Purysko
    • 1
    Email author
  • Cristina Magi-Galluzzi
    • 2
  • Omar Y. Mian
    • 3
  • Sarah Sittenfeld
    • 3
  • Elai Davicioni
    • 4
  • Marguerite du Plessis
    • 4
  • Christine Buerki
    • 4
  • Jennifer Bullen
    • 5
  • Lin Li
    • 6
  • Anant Madabhushi
    • 6
  • Andrew Stephenson
    • 7
  • Eric A. Klein
    • 7
  1. 1.Abdominal Imaging Section and Nuclear Radiology Department, Imaging InstituteCleveland ClinicClevelandUSA
  2. 2.Pathology and Laboratory Medicine InstituteCleveland ClinicClevelandUSA
  3. 3.Radiation Oncology and Translational OncologyCleveland ClinicClevelandUSA
  4. 4.GenomeDx BiosciencesVancouverCanada
  5. 5.Quantitative Health SciencesCleveland ClinicClevelandUSA
  6. 6.Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUSA
  7. 7.Glickman Urological and Kidney InstituteCleveland ClinicClevelandUSA

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