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

Prostate Cancer and Prostatic Diseases (2025)Cite this article

Subjects

Abstract

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/.

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Fig. 1: Results of heterogeneity test among the studies involving AI and standalone human readers.
Fig. 2: Summary receiver operating characteristic curve (SROC) analysis of human-AI versus human-alone for the diagnosis of clinically significant prostate cancer (csPCa).
Fig. 3: Results of pairwise comparison of sensitivity, specificity, positive predictive value (PPV), Negative Predictive Value (NPV) and cancer detection rate (CDR) between AI and human readers in threshold-predefined performance.
Fig. 4: Subgroup analysis of the impactive factors of heterogeneity in AI-acted performance.
Fig. 5

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Data availability

Deidentified blinded raw data used to conduct the retrospective and prospective analyses can be made available upon request to YDZ.

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Funding

This work was supported by the National Natural Science Foundation of China (Y.H.; 82302308).

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Author notes

  1. These authors contributed equally: Zhen Li, Yu-Dong Zhang.

Authors and Affiliations

  1. Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China

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

  2. Shanghai Normal Medical Technology Co, Ltd, Putuo, Shanghai, PR China

    Wei Xi

  3. Department of Radiology, Tongji Hospital Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China

    Zhen Li

  4. The Affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, Jiangsu, China

    Yu-Dong Zhang

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Contributions

Study conception: Xin-Ru Xie, Yu-Dong Zhang. Data collection: Xin-Ru Xie, Ying Hou, Shuai Shan, Rui Zhi, Chen-Jiang Wu, Yi-Fan Xia, Wei Xi, Zhen Li, Yu-Dong Zhang. Data analysis: Xin-Ru Xie, Ying Hou, Shuai Shan, Rui Zhi, Chen-Jiang Wu, Yi-Fan Xia, Wei Xi, Zhen Li, Yu-Dong Zhang. Technical support: Wei Xi, Yu-Dong Zhang. Administrative support: Xin-Ru Xie, Yu-Dong Zhang. Manuscript drafting: Ying Hou, Yu-Dong Zhang. All authors read and approved the final version of the manuscript.

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Correspondence to Yu-Dong Zhang.

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Xie, XR., Hou, Y., Shan, S. et al. AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis. Prostate Cancer Prostatic Dis (2025). https://doi.org/10.1038/s41391-025-01053-9

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