Scholarlink

Objective <p>This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate cancer and detecting clinically significant prostate cancer (csPCa, defined as Gleason score ≥7) using systematic review and meta-analysis methods.</p> Methods <p>Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched for predictive studies using machine learning-based MRI imaging for prostate cancer diagnosis. Sensitivity, specificity, and area under the curve (AUC) were used to assess the diagnostic accuracy of machine learning-based MRI imaging for both benign/malignant prostate cancer and csPCa.</p> Results <p>A total of 12 studies met the inclusion criteria, with 3474 patients included in the meta-analysis. Machine learning-based MRI imaging demonstrated good diagnostic value for both benign/malignant prostate cancer and csPCa. The pooled sensitivity and specificity for diagnosing benign/malignant prostate cancer were 0.92 (95% CI: 0.83–0.97) and 0.90 (95% CI: 0.68–0.97), respectively, with a combined AUC of 0.96 (95% CI: 0.94–0.98). For csPCa diagnosis, the pooled sensitivity and specificity were 0.83 (95% CI: 0.77–0.87) and 0.73 (95% CI: 0.65–0.81), respectively, with a combined AUC of 0.86 (95% CI: 0.83–0.89).</p> Conclusion <p>Machine learning-based MRI imaging shows good diagnostic accuracy for both benign/malignant prostate cancer and csPCa. Further in-depth studies are needed to validate these findings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

查看全文
  • Yusheng Zhao,
  • Lei Zhang,
  • Subo Zhang,
  • Jiajing Li,
  • Kaimin Shi,
  • Di Yao,
  • Qiuzi Li,
  • Tao Zhang,
  • Lei Xu,
  • Lei Geng,
  • Yi Sun,
  • Jinxin Wan

摘要

Objective

This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate cancer and detecting clinically significant prostate cancer (csPCa, defined as Gleason score ≥7) using systematic review and meta-analysis methods.

Methods

Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched for predictive studies using machine learning-based MRI imaging for prostate cancer diagnosis. Sensitivity, specificity, and area under the curve (AUC) were used to assess the diagnostic accuracy of machine learning-based MRI imaging for both benign/malignant prostate cancer and csPCa.

Results

A total of 12 studies met the inclusion criteria, with 3474 patients included in the meta-analysis. Machine learning-based MRI imaging demonstrated good diagnostic value for both benign/malignant prostate cancer and csPCa. The pooled sensitivity and specificity for diagnosing benign/malignant prostate cancer were 0.92 (95% CI: 0.83–0.97) and 0.90 (95% CI: 0.68–0.97), respectively, with a combined AUC of 0.96 (95% CI: 0.94–0.98). For csPCa diagnosis, the pooled sensitivity and specificity were 0.83 (95% CI: 0.77–0.87) and 0.73 (95% CI: 0.65–0.81), respectively, with a combined AUC of 0.86 (95% CI: 0.83–0.89).

Conclusion

Machine learning-based MRI imaging shows good diagnostic accuracy for both benign/malignant prostate cancer and csPCa. Further in-depth studies are needed to validate these findings.