Background AI is increasingly integrated within prostate cancer diagnosis pathway. Purpose To provide estimates of diagnostic accuracy of AI assistance for clinically significant prostate cancer (csPCa) via MRI. Materials and methods A systematic search of PubMed, Embase, Cochrane, Scopus and Web of Science from January 2017 to October 2024 was performed for studies on the diagnostic utility of AI for prostate MRI. Diagnostic performance metrics were synthesized through hierarchical summary receiver operating characteristic modeling with random-effects assumptions. Specially, to test inferiority and potential superiority of AI, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), cancer detection rate (CDR), and accuracy was pairwisely compared between AI and radiologists in study level using odds ratios (ORs) with Z-statistics. Results 7398 patients from 29 studies with AI-vs-human pairwise comparison were included. When acting as an assistant to human readers, AI demonstrated superior performance compared to stand-alone human readers in diagnosing csPCa via MRI, specifically with higher sensitivity (86.5% vs 82.6%, P = 0.001), specificity (57.8% vs 50.0%, P = 0.028), PPV (64.3% vs 58.9%, P = 0.001), and NPV (82.9% vs 76.5%, P = 0.001) while maintaining comparable CDR (40.5% vs 38.6%, P = 0.093). When used as standalone readers, AI exhibited higher specificity (58.7% vs 48.7%, P = 0.026) but at the cost of reduced sensitivity (87.2% vs 90.1%, P = 0.017). Subgroup analysis indicated that readers of varying experience levels could all improve their diagnostic performance with AI assistance. Conclusion Integrating AI as an assistant in csPCa diagnostic workflows could enhance accuracy, particularly for less experienced readers. Clinical Trial Registration information Trial Name: The efficiency comparison of radiologists with or without assistance of artificial intelligence in prostate cancer diagnosis: a meta-analysis. Registration date: April 17, 2024. Registration number: CRD42024533016. Registration information available at: <a href="https://www.crd.york.ac.uk/PROSPERO/">https://www.crd.york.ac.uk/PROSPERO/</a>.

AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis

期刊论文
发表日期：

2025年11月21日

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Xin-Ru Xie,
Ying Hou,
Shuai Shan,
Rui Zhi,
Chen-Jiang Wu,
Yi-Fan Xia,
Wei Xi,
Zhen Li,
Yu-Dong Zhang

摘要

Background

AI is increasingly integrated within prostate cancer diagnosis pathway.

Purpose

To provide estimates of diagnostic accuracy of AI assistance for clinically significant prostate cancer (csPCa) via MRI.

Materials and methods

A systematic search of PubMed, Embase, Cochrane, Scopus and Web of Science from January 2017 to October 2024 was performed for studies on the diagnostic utility of AI for prostate MRI. Diagnostic performance metrics were synthesized through hierarchical summary receiver operating characteristic modeling with random-effects assumptions. Specially, to test inferiority and potential superiority of AI, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), cancer detection rate (CDR), and accuracy was pairwisely compared between AI and radiologists in study level using odds ratios (ORs) with Z-statistics.

Results

7398 patients from 29 studies with AI-vs-human pairwise comparison were included. When acting as an assistant to human readers, AI demonstrated superior performance compared to stand-alone human readers in diagnosing csPCa via MRI, specifically with higher sensitivity (86.5% vs 82.6%, P = 0.001), specificity (57.8% vs 50.0%, P = 0.028), PPV (64.3% vs 58.9%, P = 0.001), and NPV (82.9% vs 76.5%, P = 0.001) while maintaining comparable CDR (40.5% vs 38.6%, P = 0.093). When used as standalone readers, AI exhibited higher specificity (58.7% vs 48.7%, P = 0.026) but at the cost of reduced sensitivity (87.2% vs 90.1%, P = 0.017). Subgroup analysis indicated that readers of varying experience levels could all improve their diagnostic performance with AI assistance.

Conclusion

Integrating AI as an assistant in csPCa diagnostic workflows could enhance accuracy, particularly for less experienced readers.

Clinical Trial Registration information

Trial Name: The efficiency comparison of radiologists with or without assistance of artificial intelligence in prostate cancer diagnosis: a meta-analysis. Registration date: April 17, 2024. Registration number: CRD42024533016. Registration information available at: https://www.crd.york.ac.uk/PROSPERO/.