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The evolving PI-RADS paradigm.

Andrea Ponsiglione1, Ivo G Schoots2,3, Anwar R Padhani4

  • 1Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.

Cancer Imaging : the Official Publication of the International Cancer Imaging Society
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Prostate MRI, guided by PI-RADS v2.1, enhances prostate cancer diagnosis but faces challenges in image quality and lesion assessment. Future developments, including AI and risk-based pathways, aim to improve accuracy and reduce unnecessary biopsies.

Keywords:
Artificial intelligenceChallengesDiagnostic pathwaysMRIPI-RADSProstate cancer

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Area of Science:

  • Radiology and Medical Imaging
  • Oncology
  • Diagnostic Medicine

Background:

  • Prostate MRI is crucial for diagnosing prostate cancer (PCa), aiding in biopsy decisions and reducing overdiagnosis.
  • The Prostate Imaging Reporting and Data System (PI-RADS) v2.1 is the current international standard for prostate MRI acquisition and interpretation.
  • PI-RADS v2.1 enhanced multiparametric MRI (mpMRI) assessments with clearer technical requirements and reporting.

Purpose of the Study:

  • To review the evidence supporting PI-RADS v2.1 use in prostate cancer diagnosis.
  • To identify the strengths, limitations, and future directions of PI-RADS v2.1.
  • To explore the potential of artificial intelligence (AI) and evolving pathways in prostate MRI interpretation.

Main Methods:

  • Literature review synthesizing evidence on PI-RADS v2.1.
  • Analysis of diagnostic performance, particularly in ruling out clinically significant PCa (csPCa).
  • Evaluation of current challenges, including image quality variability, PI-RADS 3 lesion management, and inter-reader agreement.

Main Results:

  • PI-RADS v2.1 demonstrates good diagnostic performance for csPCa detection and risk-adapted biopsies.
  • Significant limitations persist, including inconsistent image quality, challenges with PI-RADS 3 lesions, and moderate specificity/inter-reader agreement.
  • Artificial intelligence (AI) shows promise for improving lesion detection and workflow, while advances support biparametric MRI adoption.

Conclusions:

  • Despite improvements, PI-RADS v2.1 has limitations requiring further development for consistent prostate cancer assessment.
  • Future directions involve integrating AI, refining risk-based pathways (e.g., PI-RADS Pathway 2026), and standardizing global biopsy practices.
  • The goal is to enhance diagnostic accuracy, reduce unnecessary interventions, and improve patient outcomes in prostate cancer management.