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Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation

  1. Author:
    Gaur, Sonia
    Lay, Nathan
    Harmon,Stephanie
    Doddakashi, Sreya
    Mehralivand, Sherif
    Argun, Burak
    Barrett, Tristan
    Bednarova, Sandra
    Girometti, Rossanno
    Karaarslan, Ercan
    Kural, Ali Riza
    Oto, Aytekin
    Purysko, Andrei S
    Antic, Tatjana
    Magi-Galluzzi, Cristina
    Saglican, Yesim
    Sioletic, Stefano
    Warren, Anne Y
    Bittencourt, Leonardo
    Fütterer, Jurgen J
    Gupta, Rajan T
    Kabakus, Ismail
    Law, Yan Mee
    Margolis, Daniel J
    Shebel, Haytham
    Westphalen, Antonio C
    Wood, Bradford J
    Pinto, Peter A
    Shih, Joanna H
    Choyke, Peter L
    Summers, Ronald M
    Turkbey, Baris

  2. Author Address

    Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA., Clinical Research Directorate/ Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany., Department of Urology, Acibadem University, Istanbul, Turkey., Department of Radiology, University of Cambridge, Cambridge, UK., Department of Radiology, University of Udine, Udine, Italy., Department of Radiology, Acibadem University, Istanbul, Turkey., Department of Radiology, University of Chicago, Chicago, IL, USA., Department of Radiology, Cleveland Clinic, Cleveland, OH, USA., Department of Pathology, University of Chicago, Chicago, IL, USA., Department of Pathology, Cleveland Clinic, Cleveland, OH, USA., Department of Pathology, Acibadem University, Istanbul, Turkey., Department of Pathology, University of Udine, Udine, Italy., Department of Pathology, University of Cambridge, Cambridge, UK., Department of Radiology, Federal Fluminense University, Rio de Janeiro, Brazil., Department of Radiology, Radboud University, Nijmegen, The Netherlands., Department of Radiology, Duke University, Durham, NC, USA., Department of Radiology, Hacettepe University, Ankara, Turkey., Department of Radiology, Singapore General Hospital, Singapore., Weill Cornell Imaging, Cornell University, New York, NY, USA., Department of Radiology, Mansoura University, Mansoura, Egypt., UCSF Department of Radiology, University of California-San Francisco, San Francisco, CA, USA., Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, MD, USA., Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.,
    1. Year: 2018
    2. Date: Sep 18
    3. Epub Date: 2018 09 18
  2. Journal: Oncotarget
    1. 9
    2. 73
    3. Pages: 33804-33817
  4. Type of Article: Article
  1. Abstract:

    For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 = 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists 39; detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.

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External Sources

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  2. DOI: 10.18632/oncotarget.26100
  3. PMID: 30333911
  4. PMCID: PMC6173466
  5. PII : 26100

Library Notes

  1. Fiscal Year: FY2017-2018
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