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Objective <p>Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers.</p> Methods <p>A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligible studies reported diagnostic accuracy metrics for AI models, with clinician comparator data extracted when available. A bivariate random-effects model was used to pool sensitivity, specificity, and AUC values. Subgroup analyses were conducted to examine diagnostic performance across different cancer types and imaging modalities, and to explore potential sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool.</p> Results <p>A total of 110 studies were included in the meta-analysis. AI models achieved pooled sensitivity and specificity of 0.85 (95% CI: 0.83–0.87) and 0.83 (95% CI: 0.80–0.86), with an AUC of 0.91 (95% CI: 0.88–0.93). Clinicians demonstrated a pooled sensitivity of 0.82 (95% CI: 0.79–0.85) and specificity of 0.68 (95% CI: 0.62–0.73), with an AUC of 0.83 (95% CI: 0.80–0.86). Subgroup analyses indicated that AI models showed overall diagnostic advantages across cancer types and imaging modalities, particularly in specificity, AUC, and diagnostic odds ratios, although clinicians demonstrated higher sensitivity in the prostate cancer and MRI subgroups.</p> Conclusion <p>AI models demonstrate strong diagnostic performance across various urological cancers and imaging modalities, showing potential as supportive tools in radiological workflows. Further prospective, standardized, and multi-center evaluations are warranted to confirm AI’s clinical utility across diverse diagnostic tasks in urological oncology.</p>

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Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Yi Shen,
  • Lei Peng,
  • Lijian Gan,
  • Song Wu

摘要

Objective

Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers.

Methods

A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligible studies reported diagnostic accuracy metrics for AI models, with clinician comparator data extracted when available. A bivariate random-effects model was used to pool sensitivity, specificity, and AUC values. Subgroup analyses were conducted to examine diagnostic performance across different cancer types and imaging modalities, and to explore potential sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool.

Results

A total of 110 studies were included in the meta-analysis. AI models achieved pooled sensitivity and specificity of 0.85 (95% CI: 0.83–0.87) and 0.83 (95% CI: 0.80–0.86), with an AUC of 0.91 (95% CI: 0.88–0.93). Clinicians demonstrated a pooled sensitivity of 0.82 (95% CI: 0.79–0.85) and specificity of 0.68 (95% CI: 0.62–0.73), with an AUC of 0.83 (95% CI: 0.80–0.86). Subgroup analyses indicated that AI models showed overall diagnostic advantages across cancer types and imaging modalities, particularly in specificity, AUC, and diagnostic odds ratios, although clinicians demonstrated higher sensitivity in the prostate cancer and MRI subgroups.

Conclusion

AI models demonstrate strong diagnostic performance across various urological cancers and imaging modalities, showing potential as supportive tools in radiological workflows. Further prospective, standardized, and multi-center evaluations are warranted to confirm AI’s clinical utility across diverse diagnostic tasks in urological oncology.