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Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI

  1. Author:
    Mehralivand, Sherif
    Harmon,Stephanie
    Shih, Joanna H
    Smith, Clayton P
    Lay, Nathan
    Argun, Burak
    Bednarova, Sandra
    Baroni, Ronaldo Hueb
    Canda, Abdullah Erdem
    Ercan, Karabekir
    Girometti, Rossano
    Karaarslan, Ercan
    Kural, Ali Riza
    Pursyko, Andrei S
    Rais-Bahrami, Soroush
    Tonso, Victor Martins
    Magi-Galluzzi, Cristina
    Gordetsky, Jennifer B
    Macarenco, Ricardo Silvestre E Silva
    Merino, Maria J
    Gumuskaya, Berrak
    Saglican, Yesim
    Sioletic, Stefano
    Warren, Anne Y
    Barrett, Tristan
    Bittencourt, Leonardo
    Coskun, Mehmet
    Knauss, Chris
    Law, Yan Mee
    Malayeri, Ashkan A
    Margolis, Daniel J
    Marko, Jamie
    Yakar, Derya
    Wood, Bradford J
    Pinto, Peter A
    Choyke, Peter L
    Summers, Ronald M
    Turkbey, Baris

  2. Author Address

    Department of Urology and Pediatric Urology, University Medical Center, Mainz, Germany., Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD., Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088., Clinical Research Directorate, Leidos Biomedical Research, Inc., Frederick, MD., Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Rockville, MD., Department of Urology, Acibadem University, Istanbul, Turkey., Department of Radiology, University of Udine, Udine, Italy., Diagnostic Imaging Department, Albert Einstein Hospital, Sao Paolo, Brazil., Department of Urology, Ko 231; University, School of Medicine, Istanbul, Turkey., Department of Radiology, Ankara City Hospital, Ankara, Turkey., Department of Radiology, Acibadem University, Istanbul, Turkey., Department of Radiology, Cleveland Clinic, Cleveland, OH., Department of Urology, University of Alabama at Birmingham, Birmingham, AL., Department of Radiology, University of Alabama at Birmingham, Birmingham, AL., O 39;Neal Comprehensive Cancer Center at UAB, University of Alabama at Birmingham, Birmingham, AL., Department of Pathology, Cleveland Clinic, Cleveland, OH., Department of Pathology, University of Alabama at Birmingham, Birmingham, AL., Present address: Department of Pathology, Vanderbilt University, Nashville, TN., Pathology Department, Albert Einstein Hospital, Sao Paolo, Brazil., Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD., Department of Pathology, Ankara Yildirim Beyazit University, School of Medicine, Ankara, Turkey., 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, University of Cambridge, Cambridge, UK., Department of Radiology, Federal Fluminense University, Rio de Janeiro, Brazil., DASA Company, Rio de Janeiro, Brazil., Department of Radiology, University of Health Sciences Dr. Beh 231;et Uz Child Disease and Pediatric Surgery Training and Research Hospital, Izmir, Turkey., Department of Radiology, Walter Reed Medical Center, Bethesda, MD., Department of Radiology, Singapore General Hospital, Singapore., Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD., Weill Cornell Imaging, Cornell University, New York, NY., Department of Radiology, Medical Imaging Centre, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands., Center for Interventional Oncology, National Cancer Institute and Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD., National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, MD.,
    1. Year: 2020
    2. Date: OCT
    3. Epub Date: 2020 08 05
  2. Journal: AJR. American journal of roentgenology
    1. 215
    2. 4
    3. Pages: 903-912
  4. Type of Article: Article
  5. ISSN: 0361-803X
  1. Abstract:

    OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.

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External Sources

  1. View Full Text
  2. DOI: 10.2214/AJR.19.22573
  3. PMID: 32755355
  4. View record in Web of Science®
  5. WOS: 000574408700025

Library Notes

  1. Fiscal Year: FY2019-2020
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