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

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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. M RI examinations f rom five i nstitutions were i ncluded 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.

Original languageEnglish (US)
Pages (from-to)903-912
Number of pages10
JournalAmerican Journal of Roentgenology
Issue number4
StatePublished - Oct 2020
Externally publishedYes


  • Artificial intelligence
  • Laparoscopic
  • Mri
  • Multiparametric
  • Prostate cancer
  • Radical prostatectomy
  • Robot-assisted

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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