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AI decision support for increasing prostate biopsy efficiency: a retrospective multicentre, multiscanner study.

Nikita Sushentsev1, Zobair Arya2, Jobie Budd2

  • 1Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK. ns784@medschl.cam.ac.uk.

European Radiology
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

An artificial intelligence decision support system (AI-DSS) can reduce unnecessary prostate biopsies by improving cancer detection efficiency. This AI-DSS enhances grade selectivity and biopsy efficiency, potentially improving prostate cancer diagnosis.

Keywords:
AI (artificial intelligence)Magnetic resonance imagingProstate cancer

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Area of Science:

  • Urology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Current prostate cancer diagnostic pathways often lead to unnecessary biopsies.
  • Improving the benefit-to-harm ratio in prostate biopsy decisions is crucial.

Purpose of the Study:

  • To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS).
  • To optimize prostate biopsy decisions and improve the benefit-to-harm ratio.

Main Methods:

  • A retrospective, multicentre study involving 1022 patients.
  • Developed an AI-DSS integrating PI-RADS scores, automated prostate-specific antigen density (PSAd), and deep-learning imaging risk scores.
  • Validated the AI-DSS on an independent cohort of 252 men, benchmarking against clinical decisions.

Main Results:

  • In the validation cohort, the AI-DSS at a 31% cancer detection rate (CDR) avoided 28 biopsies while missing one significant cancer (≥ GG2).
  • This resulted in a 70% increase in grade selectivity and a 79% increase in biopsy efficiency.
  • At a 30% CDR, grade selectivity and biopsy efficiency increased by 172% and 236%, respectively, with four significant cancers missed.

Conclusions:

  • An AI-DSS integrating clinical and advanced imaging data improves the benefit-to-harm ratio of prostate biopsy decisions retrospectively.
  • Prospective validation within real-world clinical workflows is necessary for clinical implementation.