Zhao Yusheng, Zhang Lei, Zhang Subo, Li Jiajing, Shi Kaimin, Yao Di, Li Qiuzi, Zhang Tao, Xu Lei, Geng Lei, Sun Yi, Wan Jinxin
Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang city, China.
Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang city, China.
Prostate Cancer Prostatic Dis. 2025 Jul 28. doi: 10.1038/s41391-025-00997-2.
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.
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.
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).
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.
本研究旨在采用系统评价和荟萃分析方法，评估基于机器学习的磁共振成像（MRI）在鉴别前列腺癌良恶性以及检测临床显著前列腺癌（csPCa，定义为 Gleason 评分≥7）方面的诊断价值。
系统检索电子数据库（PubMed、Web of Science、Cochrane 图书馆和 Embase），查找使用基于机器学习的 MRI 成像进行前列腺癌诊断的预测性研究。采用灵敏度、特异度和曲线下面积（AUC）评估基于机器学习的 MRI 成像对前列腺癌良恶性及 csPCa 的诊断准确性。
共有 12 项研究符合纳入标准，荟萃分析纳入 3474 例患者。基于机器学习的 MRI 成像对前列腺癌良恶性及 csPCa 均显示出良好的诊断价值。诊断前列腺癌良恶性的合并灵敏度和特异度分别为 0.92（95%CI：0.83 - 0.97）和 0.90（95%CI：0.68 - 0.97），合并 AUC 为 0.96（95%CI：0.94 - 0.98）。对于 csPCa 诊断，合并灵敏度和特异度分别为 0.83（95%CI：0.77 - 0.87）和 0.73（95%CI：0.65 - 0.81），合并 AUC 为 0.86（95%CI：0.83 - 0.89）。
基于机器学习的 MRI 成像对前列腺癌良恶性及 csPCa 均显示出良好的诊断准确性。需要进一步深入研究以验证这些发现。
