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Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists.

Methods

Nine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone—peripheral (PZ) and transition (TZ).

Results

Index lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001).

Conclusions

CAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience.

Key Points

• Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI.

• CAD assistance improves agreement between radiologists in detecting prostate cancer lesions.

• However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone.

• CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence.

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Abbreviations

AUC:

area under the curve

CAD:

computer-aided diagnosis

DCE:

dynamic contrast-enhanced imaging

DWI:

diffusion-weighted imaging

GS:

Gleason score

ISA:

index of specific agreement

mpMRI:

multi-parametric MRI

PI-RADS:

Prostate Imaging Reporting and Data System

PSA:

prostate-specific antigen

PZ:

peripheral zone

T2W:

T2-weighted

TRUS:

transrectal ultrasound

TZ:

transition zone

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Funding

The study has received funding by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research (Grant ZIA BC 010655).

Author information

Authors and Affiliations

  1. Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA

    Matthew D. Greer, Francesca V. Mertan, Peter L. Choyke & Baris Turkbey

  2. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA

    Nathan Lay & Ronald M. Summers

  3. Biometric Research Program, NCI, NIH, Bethesda, MD, USA

    Joanna H. Shih

  4. Department of Radiology, University of Cambridge School of Medicine, Cambridge, UK

    Tristan Barrett

  5. Universidade Federal Fluminense and CDPI Clinics/DASA, Rio de Janeiro, RJ, Brazil

    Leonardo Kayat Bittencourt

  6. George Washington University Hospital, Washington, DC, USA

    Samuel Borofsky

  7. Hacettepe University, Ankara, Turkey

    Ismail Kabakus

  8. Singapore General Hospital, Singapore, Singapore

    Yan Mee Law

  9. Radiology and Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA

    Jamie Marko

  10. Department of Radiology, Nephrology Center, Mansoura University, Mansoura, 35516, Egypt

    Haytham Shebel

  11. Laboratory of Pathology, NCI, NIH, Bethesda, MD, USA

    Maria J. Merino

  12. Center for Interventional Oncology, NCI and Radiology Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA

    Bradford J. Wood

  13. Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA

    Peter A. Pinto

Authors
  1. Matthew D. Greer
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  2. Nathan Lay
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  3. Joanna H. Shih
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  4. Tristan Barrett
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  5. Leonardo Kayat Bittencourt
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  6. Samuel Borofsky
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  7. Ismail Kabakus
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  8. Yan Mee Law
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  9. Jamie Marko
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  10. Haytham Shebel
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  12. Maria J. Merino
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  13. Bradford J. Wood
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  14. Peter A. Pinto
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  15. Ronald M. Summers
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  16. Peter L. Choyke
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  17. Baris Turkbey
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Corresponding author

Correspondence to Baris Turkbey.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Baris Turkbey, MD.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Bradford Wood, Philips and InVivo; Ronald Summers, Ping An and iCAD.

Statistics and biometry

One of the authors, Dr. Joanna Shih, has significant statistical expertise.

Ethical approval

Institutional review board approval was obtained.

Informed consent

Written informed consent was obtained from all patients in this study.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Greer MD, Shih JH, Lay N, et al. Validation of the dominant sequence paradigm and role of dynamic contrast-enhanced imaging in PI-RADS Version 2. Radiology. 2017;285:859–869.

Methodology

• retrospective

• diagnostic study

• multicentre study

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Greer, M.D., Lay, N., Shih, J.H. et al. Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. Eur Radiol 28, 4407–4417 (2018). https://doi.org/10.1007/s00330-018-5374-6

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  • DOI: https://doi.org/10.1007/s00330-018-5374-6

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