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Objectives <p>To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS) for optimising prostate biopsy decisions and improving benefit-to-harm ratios.</p> Materials and methods <p>This retrospective, multicentre, multiscanner study used data from 1022 patients. An AI-DSS integrating PI-RADS scores, automated prostate-specific antigen density (PSAd), and deep-learning imaging risk scores was developed on 770 cases and validated on an independent cohort of 252 men from six UK centres. The AI-DSS performance was benchmarked against the real-world clinical decisions (reference standard) using grade selectivity, biopsy efficiency, and selective biopsy avoidance as outcome measures. Biopsy-proven detection of grade group (GG) ≥ 2 disease was the reference standard.</p> Results <p>In the validation cohort of 252 patients (mean age, 67.3 years), 137 underwent biopsy and 79 (31%) harboured ≥ GG2 disease. Compared to the reference standard, the AI-DSS at the 31% cancer detection rate (CDR) would have avoided 28 biopsies while missing one ≥ GG2 cancer. This corresponded to a 70% increase in grade selectivity (from 4.6 to 7.8), 79% increase in biopsy efficiency (from 1.4 to 2.5), and a 143% increase in selective biopsy avoidance (from 2.8 to 6.8). At the reduced CDR of 30%, grade selectivity, biopsy efficiency, and selective biopsy avoidance increased by 172%, 236%, and 475%, with four ≥ GG2 cancers missed.</p> Conclusion <p>An AI-DSS that integrates clinical and advanced imaging data improves the benefit-to-harm ratio of prostate biopsy decisions in a retrospective setting. Future prospective validation as part of real-world clinical workflow is required to enable clinical implementation.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Current prostate cancer diagnostic pathways result in fewer unnecessary biopsies. Can an AI decision support system (AI-DSS) further improve biopsy efficiency for detecting significant cancer?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>An AI-DSS avoided 28 biopsies in a 252-patient cohort, increasing grade selectivity, biopsy efficiency, and selective biopsy avoidance by 70%, 79%, and 143%, respectively.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Integrating an AI-DSS into clinical workflows may further reduce unnecessary prostate biopsies and overdiagnosis of indolent disease, thus potentially improving the efficiency of the prostate cancer diagnostic pathway.</i></p> Graphical Abstract <p></p>

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

  • 期刊论文

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  • 发表日期：

    2026年2月20日

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  • Nikita Sushentsev,
  • Zobair Arya,
  • Jobie Budd,
  • Amy Frary,
  • Nadia Moreira da Silva,
  • Mirjana Ferrer Rodriguez,
  • Paul Burn,
  • Richard Hindley,
  • Nikhil Vasdev,
  • Mohamed Ibrahim,
  • Alison Bradley,
  • Adrian Andreou,
  • Sidath Liyanage,
  • Raj Persad,
  • Jonathan Aning,
  • Alexander B. C. D. Ng,
  • Aqua Asif,
  • Veeru Kasivisvanathan,
  • Tristan Barrett,
  • Mark Hinton,
  • Anwar Roshanali Padhani,
  • Aarti Shah,
  • Lucy Davies,
  • Antony Rix,
  • Evis Sala

摘要

Objectives

To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS) for optimising prostate biopsy decisions and improving benefit-to-harm ratios.

Materials and methods

This retrospective, multicentre, multiscanner study used data from 1022 patients. An AI-DSS integrating PI-RADS scores, automated prostate-specific antigen density (PSAd), and deep-learning imaging risk scores was developed on 770 cases and validated on an independent cohort of 252 men from six UK centres. The AI-DSS performance was benchmarked against the real-world clinical decisions (reference standard) using grade selectivity, biopsy efficiency, and selective biopsy avoidance as outcome measures. Biopsy-proven detection of grade group (GG) ≥ 2 disease was the reference standard.

Results

In the validation cohort of 252 patients (mean age, 67.3 years), 137 underwent biopsy and 79 (31%) harboured ≥ GG2 disease. Compared to the reference standard, the AI-DSS at the 31% cancer detection rate (CDR) would have avoided 28 biopsies while missing one ≥ GG2 cancer. This corresponded to a 70% increase in grade selectivity (from 4.6 to 7.8), 79% increase in biopsy efficiency (from 1.4 to 2.5), and a 143% increase in selective biopsy avoidance (from 2.8 to 6.8). At the reduced CDR of 30%, grade selectivity, biopsy efficiency, and selective biopsy avoidance increased by 172%, 236%, and 475%, with four ≥ GG2 cancers missed.

Conclusion

An AI-DSS that integrates clinical and advanced imaging data improves the benefit-to-harm ratio of prostate biopsy decisions in a retrospective setting. Future prospective validation as part of real-world clinical workflow is required to enable clinical implementation.

Key Points

Question Current prostate cancer diagnostic pathways result in fewer unnecessary biopsies. Can an AI decision support system (AI-DSS) further improve biopsy efficiency for detecting significant cancer?

Findings An AI-DSS avoided 28 biopsies in a 252-patient cohort, increasing grade selectivity, biopsy efficiency, and selective biopsy avoidance by 70%, 79%, and 143%, respectively.

Clinical relevance Integrating an AI-DSS into clinical workflows may further reduce unnecessary prostate biopsies and overdiagnosis of indolent disease, thus potentially improving the efficiency of the prostate cancer diagnostic pathway.

Graphical Abstract