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Related Experiment Video

Updated: May 30, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically

Eric H Kim1,2, Huaping Jing3, Kainen L Utt3

  • 1Division of Urology, Department of Surgery, University of Nevada Reno School of Medicine, Reno, Nevada.

The Journal of Urology
|January 27, 2025
PubMed
Summary
This summary is machine-generated.

Diffusion basis spectrum imaging (DBSI) with AI accurately predicts clinically significant prostate cancer (csPCa). Combining DBSI and PI-RADS may reduce unnecessary biopsies for prostate cancer.

Keywords:
MRIPSAbiomarkersprostate cancer

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Conventional prostate MRI has limitations in accurately detecting clinically significant prostate cancer (csPCa).
  • Diffusion basis spectrum imaging (DBSI) offers advanced metrics for prostate cancer assessment.
  • Biopsy remains the gold standard but carries risks and costs.

Purpose of the Study:

  • To evaluate the efficacy of an artificial intelligence (AI) model utilizing DBSI metrics for predicting csPCa.
  • To compare the diagnostic performance of the DBSI-AI model against established biomarkers like PSA density (PSAD) and PI-RADS.
  • To assess the potential of the DBSI-AI model to reduce unnecessary prostate biopsies.

Main Methods:

  • 241 patients underwent MRI with conventional and DBSI sequences prior to biopsy.
  • AI models were trained using DBSI metrics, with biopsy pathology as the ground truth.
  • The DBSI-AI model's performance was compared with PSAD and PI-RADS for csPCa risk discrimination (Gleason score >= 7).

Main Results:

  • The DBSI-AI model independently predicted csPCa (OR 2.04, P < .01).
  • DBSI-AI model alone showed performance similar to PSAD + PI-RADS (AUC 0.863 vs 0.859).
  • The combination of DBSI-AI model + PI-RADS achieved the highest risk discrimination (AUC 0.894, P < .01).
  • A strategy using DBSI-AI for PI-RADS 1-3 could reduce biopsies by 27% while missing 2% of csPCa.

Conclusions:

  • An AI model based on DBSI accurately predicts csPCa.
  • Combining the DBSI-AI model with PI-RADS can enhance risk stratification.
  • This approach holds promise for reducing unnecessary prostate biopsies.