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Objective <p>Multiparametric magnetic resonance imaging (mpMRI) detects clinically significant prostate cancer (csPCa, Gleason Grade Group ≥ 2) with high sensitivity but limited specificity and inter-reader variability. Artificial intelligence (AI), particularly convolutional neural networks (CNNs) and deep learning, may improve diagnostic consistency and accuracy. This meta-analysis compares AI systems and experienced radiologists in detecting csPCa using mpMRI.</p> Methods <p>We performed a systematic review and meta-analysis of English-language non-RCT studies. PubMed, Embase, and Cochrane databases were searched up to May 2025, yielding 855 studies. Only studies comparing CNN-based or deep-learning AI models to radiologists were included. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating curve (AUC) were calculated using a bivariate random-effects model.</p> Results <p>Ten studies with 2586 patients were analyzed. AI systems showed pooled sensitivity of 0.90 (95% CI 0.84–0.94) and specificity of 0.69 (95% CI 0.45–0.85). Radiologists had a sensitivity of 0.89 (95% CI 0.82–0.94) and a specificity of 0.60 (95% CI 0.43–0.75). DOR was 17.54 (95% CI 9.34–32.94) for AI and 12.35 (95% CI 4.96–30.76) for radiologists. Summary receiver operating characteristic (SROC) curves indicated similar diagnostic accuracy, with AI slightly outperforming radiologists (AUC 0.88 vs. 0.85).</p> Conclusion <p>AI systems perform comparably to radiologists in detecting csPCa on mpMRI, with a potential edge in specificity, though confidence intervals overlapped. High heterogeneity and the retrospective nature of all included studies limit reliability, necessitating prospective validation. AI could serve as an adjunct in prostate cancer diagnosis, potentially improving precision and reducing unnecessary biopsies with further model refinement.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> Interpretation of mpMRI for clinically significant prostate cancer varies among radiologists, affecting diagnostic consistency.</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> Meta-analysis shows AI has comparable sensitivity and potentially superior specificity to radiologists in detecting significant prostate cancer on mpMRI, though confidence intervals overlapped.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis><i> AI has the potential to enhance diagnostic accuracy, reduce unnecessary biopsies, and improve consistency in prostate cancer detection, thereby supporting more reliable and standardized imaging assessments across centers.</i></p> Graphical Abstract <p></p>

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Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Marco Antonio Andrade,
  • Henrique Rodrigues,
  • Caio Hernandes Colhado,
  • Nathan Joseph Silva Godinho,
  • Rhuan Dorigueto Dos Santos,
  • Alexandre Leite de Andrade,
  • Taylor Goodstein

摘要

Objective

Multiparametric magnetic resonance imaging (mpMRI) detects clinically significant prostate cancer (csPCa, Gleason Grade Group ≥ 2) with high sensitivity but limited specificity and inter-reader variability. Artificial intelligence (AI), particularly convolutional neural networks (CNNs) and deep learning, may improve diagnostic consistency and accuracy. This meta-analysis compares AI systems and experienced radiologists in detecting csPCa using mpMRI.

Methods

We performed a systematic review and meta-analysis of English-language non-RCT studies. PubMed, Embase, and Cochrane databases were searched up to May 2025, yielding 855 studies. Only studies comparing CNN-based or deep-learning AI models to radiologists were included. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating curve (AUC) were calculated using a bivariate random-effects model.

Results

Ten studies with 2586 patients were analyzed. AI systems showed pooled sensitivity of 0.90 (95% CI 0.84–0.94) and specificity of 0.69 (95% CI 0.45–0.85). Radiologists had a sensitivity of 0.89 (95% CI 0.82–0.94) and a specificity of 0.60 (95% CI 0.43–0.75). DOR was 17.54 (95% CI 9.34–32.94) for AI and 12.35 (95% CI 4.96–30.76) for radiologists. Summary receiver operating characteristic (SROC) curves indicated similar diagnostic accuracy, with AI slightly outperforming radiologists (AUC 0.88 vs. 0.85).

Conclusion

AI systems perform comparably to radiologists in detecting csPCa on mpMRI, with a potential edge in specificity, though confidence intervals overlapped. High heterogeneity and the retrospective nature of all included studies limit reliability, necessitating prospective validation. AI could serve as an adjunct in prostate cancer diagnosis, potentially improving precision and reducing unnecessary biopsies with further model refinement.

Key Points

Question Interpretation of mpMRI for clinically significant prostate cancer varies among radiologists, affecting diagnostic consistency.

Findings Meta-analysis shows AI has comparable sensitivity and potentially superior specificity to radiologists in detecting significant prostate cancer on mpMRI, though confidence intervals overlapped.

Clinical relevance AI has the potential to enhance diagnostic accuracy, reduce unnecessary biopsies, and improve consistency in prostate cancer detection, thereby supporting more reliable and standardized imaging assessments across centers.

Graphical Abstract