Zhang Xin, Qi Yiting, Wang Xi, Chen Haowen, Li Jia
Department of Nursing, Zhuhai Campus of Zunyi Medical University, 368 Jinhaian Community, Sanzao Town, Jinwan District, Zhuhai, Guangdong Province, 519000, China, 86 137 2625 6630.
Department of Ultrasound Imaging, Zhuhai People's Hospital, The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University, Zhuhai, Guangdong, China.
J Med Internet Res. 2025 Dec 9;27:e80981. doi: 10.2196/80981.
Artificial intelligence (AI) techniques, particularly those using machine learning and deep learning to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE) in prostate cancer.
This meta-analysis compares the diagnostic performance of AI-enabled imaging techniques with that of radiologists for predicting preoperative EPE in prostate cancer.
We conducted a systematic literature search in PubMed, Embase, and Web of Science up to September 2025, following PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy) guidelines. Studies applying AI techniques to predict EPE using multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA PET) imaging were included. Sensitivity, specificity, and area under the curve (AUC) for both internal and external validation sets were extracted and pooled using a bivariate random effects model. Study quality was assessed using the modified Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool.
A total of 21 studies were included in the analysis. For internal validation sets in patient-based analyses, mpMRI-based AI demonstrated a pooled sensitivity of 0.77 (95% CI 0.71-0.82), specificity of 0.71 (95% CI 0.64-0.78), and AUC of 0.81 (95% CI 0.77-0.84). In external validation, mpMRI-based AI achieved a sensitivity of 0.66 (95% CI 0.43-0.84), specificity of 0.80 (95% CI 0.64-0.90), and AUC of 0.80 (95% CI 0.77-0.84). In comparison, radiologists achieved a pooled sensitivity of 0.69 (95% CI 0.60-0.76), specificity of 0.73 (95% CI 0.66-0.78), and AUC of 0.77 (95% CI 0.73-0.80). Statistical comparisons between mpMRI-based AI and radiologists showed no significant difference in sensitivity (Z=1.61; P=.10), specificity (Z=0.43; P=.67). Conversely, the AUC of mpMRI-based AI was significantly higher than that of PSMA PET-based (Z=2.77; P=.01). PSMA PET-based AI showed moderate performance with sensitivity of 0.73 (95% CI 0.65-0.80), specificity of 0.61 (95% CI 0.30-0.85), and AUC of 0.74 (95% CI 0.70-0.77) in internal validation, and in external validation, it demonstrated sensitivity of 0.77 (95% CI 0.57-0.89) and specificity of 0.50 (95% CI 0.22-0.78), demonstrating no significant advantage over radiologists.
mpMRI-based AI demonstrated improved diagnostic performance for preoperative prediction of EPE in prostate cancer compared to conventional radiological assessment, achieving higher AUC. However, PSMA PET-based AI models currently offer no significant advantage over either mpMRI-based AI or radiologists. Limitations include the retrospective design and high heterogeneity, which may introduce bias and affect generalizability. Larger, more diverse cohorts are essential for confirming these findings and optimizing the integration of AI in clinical practice.
人工智能（AI）技术，特别是那些使用机器学习和深度学习来分析多模态成像数据的技术，在增强前列腺癌前列腺外扩展（EPE）的术前预测方面显示出了巨大的前景。
本荟萃分析比较了基于AI的成像技术与放射科医生在预测前列腺癌术前EPE方面的诊断性能。
我们按照PRISMA-DTA（诊断试验准确性系统评价和荟萃分析的首选报告项目）指南，在截至2025年9月的PubMed、Embase和Web of Science中进行了系统的文献检索。纳入了应用AI技术使用多参数磁共振成像（mpMRI）和前列腺特异性膜抗原正电子发射断层扫描（PSMA PET）成像来预测EPE的研究。使用双变量随机效应模型提取并汇总内部和外部验证集的敏感性、特异性和曲线下面积（AUC）。使用改良的诊断性能研究质量评估（QUADAS-2）工具评估研究质量。
分析共纳入21项研究。在基于患者的分析中的内部验证集方面，基于mpMRI的AI显示汇总敏感性为0.77（95%CI 0.71-0.82），特异性为0.71（95%CI 0.64-0.78），AUC为0.81（95%CI 0.77-0.84）。在外部验证中，基于mpMRI的AI的敏感性为0.66（95%CI 0.43-0.84），特异性为0.80（95%CI 0.64-0.90），AUC为0.80（95%CI 0.77-0.84）。相比之下，放射科医生的汇总敏感性为0.69（95%CI 0.60-0.76），特异性为0.73（95%CI 0.66-0.78），AUC为0.77（95%CI 0.73-0.80）。基于mpMRI的AI与放射科医生之间的统计比较显示，敏感性（Z=1.61；P= .10）、特异性（Z=0.43；P= .67）无显著差异。相反，基于mpMRI的AI的AUC显著高于基于PSMA PET的AI（Z=2.77；P= .01）。基于PSMA PET的AI在内部验证中的表现中等，敏感性为0.73（95%CI 0.65-0.80），特异性为0.61（95%CI 0.30-0.85），AUC为0.74（95%CI 0.70-0.77），在外部验证中，其敏感性为0.77（95%CI 0.57-0.89），特异性为0.50（95%CI 0.22-0.78），与放射科医生相比无显著优势。
与传统放射学评估相比，基于mpMRI的AI在前列腺癌术前EPE预测方面显示出更好的诊断性能，AUC更高。然而，基于PSMA PET的AI模型目前与基于mpMRI的AI或放射科医生相比均无显著优势。局限性包括回顾性设计和高度异质性，这可能会引入偏差并影响可推广性。更大、更多样化的队列对于证实这些发现以及优化AI在临床实践中的整合至关重要。
