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Objectives <p>Non-contrast MRI (bi-parametric MRI—bpMRI) has been investigated as a potential tool to be integrated in clinically significant prostate cancer (csPCa) screening. Moreover, artificial intelligence (AI) is emerging too as a potential support, especially for less-experienced radiologists. Therefore, the aim of this study was to evaluate the effectiveness of an AI-based software in csPCa screening using bpMRI, with a focus on supporting less-experienced radiologists.</p> Material and methods <p>A retrospective analysis was conducted within the PROSA-trial, a randomized, single-center study involving 759 men eligible for PCa screening. BpMRI were acquired using prostate imaging reporting and data system (PI-RADS) v2.1-compliant protocols and evaluated independently by an expert radiologist, a less-experienced reader, AI-based software, and the less-experienced reader with AI support. Diagnostic performance was assessed using ROC curves and inter-reader agreement (Cohen’s kappa), using expert interpretation as the reference standard.</p> Results <p>Four hundred ninety-nine bpMRI were analyzed. The AI-assisted less-experienced reader achieved the highest diagnostic performance (sensitivity 76.5%, specificity 97.2%, accuracy 95.8%, AUC 0.969), surpassing both AI-alone (sensitivity 58.8%, specificity 96.6%, accuracy 94.0%, AUC 0.952) and unaided less-experienced reader (sensitivity 67.6%, specificity 95.1%, accuracy 93.2%, AUC 0.932). Inter-reader agreement improved with AI assistance (from κ = 0.64 to 0.84). AI assistance reduced equivocal PI-RADS 3 cases (from 77 to 53) and improved exact agreement with the expert from 32.5% to 54.5%, while also reducing diagnostic discordance.</p> Conclusions <p>AI can support less experienced radiologists and enhance consistency in bpMRI interpretation, especially considering equivocal cases. Moreover, integrating AI into radiology workflows can alleviate reporting burden and help prioritize suspicious cases, offering critical advantages in high-volume PCa screening settings.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can AI improve the diagnostic performance and consistency of less experienced radiologists interpreting non-contrast prostate MRI in a screening setting</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>AI assistance significantly improved effectiveness and inter-reader agreement in prostate MRI interpretation, particularly for less experienced radiologists within a screening population</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Integrating AI into prostate MRI workflows may enhance screening efficiency, reduce variability, and support equitable early detection of csPCa, with a positive impact on prioritization of reporting</i>.</p> Graphical Abstract <p></p>

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Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial

  • 期刊论文

  • 发表日期：

    2026年2月13日

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  • Emanuele Messina,
  • Antonella Borrelli,
  • Francesca Mezzapesa,
  • Ludovica Laschena,
  • Sara Lucciola,
  • Simone Novelli,
  • Nicholas Landini,
  • Martina Pecoraro,
  • Alessandro Sciarra,
  • Daniele Santini,
  • Valeria Panebianco

摘要

Objectives

Non-contrast MRI (bi-parametric MRI—bpMRI) has been investigated as a potential tool to be integrated in clinically significant prostate cancer (csPCa) screening. Moreover, artificial intelligence (AI) is emerging too as a potential support, especially for less-experienced radiologists. Therefore, the aim of this study was to evaluate the effectiveness of an AI-based software in csPCa screening using bpMRI, with a focus on supporting less-experienced radiologists.

Material and methods

A retrospective analysis was conducted within the PROSA-trial, a randomized, single-center study involving 759 men eligible for PCa screening. BpMRI were acquired using prostate imaging reporting and data system (PI-RADS) v2.1-compliant protocols and evaluated independently by an expert radiologist, a less-experienced reader, AI-based software, and the less-experienced reader with AI support. Diagnostic performance was assessed using ROC curves and inter-reader agreement (Cohen’s kappa), using expert interpretation as the reference standard.

Results

Four hundred ninety-nine bpMRI were analyzed. The AI-assisted less-experienced reader achieved the highest diagnostic performance (sensitivity 76.5%, specificity 97.2%, accuracy 95.8%, AUC 0.969), surpassing both AI-alone (sensitivity 58.8%, specificity 96.6%, accuracy 94.0%, AUC 0.952) and unaided less-experienced reader (sensitivity 67.6%, specificity 95.1%, accuracy 93.2%, AUC 0.932). Inter-reader agreement improved with AI assistance (from κ = 0.64 to 0.84). AI assistance reduced equivocal PI-RADS 3 cases (from 77 to 53) and improved exact agreement with the expert from 32.5% to 54.5%, while also reducing diagnostic discordance.

Conclusions

AI can support less experienced radiologists and enhance consistency in bpMRI interpretation, especially considering equivocal cases. Moreover, integrating AI into radiology workflows can alleviate reporting burden and help prioritize suspicious cases, offering critical advantages in high-volume PCa screening settings.

Key Points

Question Can AI improve the diagnostic performance and consistency of less experienced radiologists interpreting non-contrast prostate MRI in a screening setting?

Findings AI assistance significantly improved effectiveness and inter-reader agreement in prostate MRI interpretation, particularly for less experienced radiologists within a screening population.

Clinical relevance Integrating AI into prostate MRI workflows may enhance screening efficiency, reduce variability, and support equitable early detection of csPCa, with a positive impact on prioritization of reporting.

Graphical Abstract