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

作者：Yusheng Zhao, Lei Zhang, Subo Zhang, Jiajing Li, Kaimin Shi, Di Yao, Qiong Li, Tao Zhang, Lei Xu, Lei Geng, Yi Sun, Jinxin Wan · 发表于：Prostate Cancer and Prostatic Diseases · 年份：2025 · DOI：10.1038/s41391-025-00997-2 · 被引用次数：12 · 研究领域：Prostate Cancer Diagnosis and Treatment、Prostate Cancer Treatment and Research、Radiomics and Machine Learning in Medical Imaging

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 ...