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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Related Experiment Video

Updated: Sep 8, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Evaluating Artificial Intelligence-Assisted Prostate Biparametric MRI Interpretation: An International Multireader

David G Gelikman1, Enis C Yilmaz1, Stephanie A Harmon1

  • 1Molecular Imaging Branch, National Cancer Institute, NIH, Bldg 10, Rm B3B85, Bethesda, MD 20892.

AJR. American Journal of Roentgenology
|July 16, 2025
PubMed
Summary

Artificial intelligence (AI) improved prostate cancer detection on biparametric MRI (bpMRI) by increasing positive predictive value and reader agreement, though sensitivity slightly decreased. This AI tool shows promise for more consistent bpMRI interpretations.

Keywords:
MRIartificial intelligencecomputer-aided diagnosismultireaderprostate cancer

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Prostate Cancer Diagnostics

Background:

  • Variability in prostate biparametric MRI (bpMRI) interpretation impacts diagnostic reliability for prostate cancer (PCa).
  • Artificial intelligence (AI) offers potential to standardize interpretations and enhance diagnostic accuracy in bpMRI.

Purpose of the Study:

  • To evaluate the impact of a deep learning AI model on lesion- and patient-level detection rates for clinically significant PCa (csPCa) and PCa.
  • To assess the effect of AI on interreader agreement in bpMRI interpretations.

Main Methods:

  • Retrospective, multireader, multicenter study involving 180 patients undergoing bpMRI.
  • Six radiologists interpreted bpMRI scans with and without AI assistance.
  • Reference standard included whole-mount pathology or systematic biopsies; lesion-level sensitivity, PPV, patient-level AUC, and interreader agreement were assessed.

Main Results:

  • AI significantly improved lesion-level positive predictive value (PPV) for both csPCa and PCa detection (p < .001).
  • AI assistance led to a slight reduction in lesion-level sensitivity (p = .01) but showed no significant difference in patient-level AUC.
  • Interreader agreement for PI-RADS scores (lesion and patient level) and lesion size measurements significantly improved with AI assistance (p < .001).

Conclusions:

  • AI integration in bpMRI interpretation enhances lesion-level PPV and interreader agreement.
  • While sensitivity slightly decreased, AI shows potential to improve consistency and reduce false positives in PCa diagnosis.
  • Further AI model optimization is needed to balance sensitivity and specificity for improved clinical impact.