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European Radiology

, Volume 28, Issue 10, pp 4407–4417 | Cite as

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

  • Matthew D. Greer
  • Nathan Lay
  • Joanna H. Shih
  • Tristan Barrett
  • Leonardo Kayat Bittencourt
  • Samuel Borofsky
  • Ismail Kabakus
  • Yan Mee Law
  • Jamie Marko
  • Haytham Shebel
  • Francesca V. Mertan
  • Maria J. Merino
  • Bradford J. Wood
  • Peter A. Pinto
  • Ronald M. Summers
  • Peter L. Choyke
  • Baris Turkbey
Magnetic Resonance

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.

Keywords

Prostate cancer MRI scans Image interpretation computer assisted Computer-assisted diagnosis 

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

Notes

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).

Compliance with ethical standards

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

Supplementary material

330_2018_5374_MOESM1_ESM.docx (18 kb)
ESM 1 (DOCX 18 kb)

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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018 2018

Authors and Affiliations

  • Matthew D. Greer
    • 1
  • Nathan Lay
    • 2
  • Joanna H. Shih
    • 3
  • Tristan Barrett
    • 4
  • Leonardo Kayat Bittencourt
    • 5
  • Samuel Borofsky
    • 6
  • Ismail Kabakus
    • 7
  • Yan Mee Law
    • 8
  • Jamie Marko
    • 9
  • Haytham Shebel
    • 10
  • Francesca V. Mertan
    • 1
  • Maria J. Merino
    • 11
  • Bradford J. Wood
    • 12
  • Peter A. Pinto
    • 13
  • Ronald M. Summers
    • 2
  • Peter L. Choyke
    • 1
  • Baris Turkbey
    • 1
  1. 1.Molecular Imaging ProgramNCI, NIHBethesdaUSA
  2. 2.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging SciencesNational Institutes of Health Clinical CenterBethesdaUSA
  3. 3.Biometric Research ProgramNCI, NIHBethesdaUSA
  4. 4.Department of RadiologyUniversity of Cambridge School of MedicineCambridgeUK
  5. 5.Universidade Federal Fluminense and CDPI Clinics/DASARio de JaneiroBrazil
  6. 6.George Washington University HospitalWashingtonUSA
  7. 7.Hacettepe UniversityAnkaraTurkey
  8. 8.Singapore General HospitalSingaporeSingapore
  9. 9.Radiology and Imaging Sciences Department, Clinical CenterNIHBethesdaUSA
  10. 10.Department of Radiology, Nephrology CenterMansoura UniversityMansouraEgypt
  11. 11.Laboratory of PathologyNCI, NIHBethesdaUSA
  12. 12.Center for Interventional Oncology, NCI and Radiology Imaging Sciences, Clinical CenterNIHBethesdaUSA
  13. 13.Urologic Oncology BranchNCI, NIHBethesdaUSA

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