Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI’s ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62–97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.
早期发现转移性前列腺癌（mPCa）至关重要。虽然前列腺特异性膜抗原（PSMA）PET扫描具有较高的诊断准确性，但其存在阅片者间差异以及报告过程耗时的问题。本系统综述已在PROSPERO平台注册（ID CRD42023456044），旨在评估人工智能在提升PSMA PET扫描对mPCa的报告、诊断及预测能力方面的作用。纳入标准涵盖使用人工智能评估PSMA PET中mPCa的研究，排除使用非PSMA示踪剂的研究。检索范围包括Medline、Embase和Scopus数据库，时间从建库至2023年7月。经过对249项研究的筛选，最终11项研究符合纳入条件。由于研究存在异质性，未进行荟萃分析。预测模型偏倚风险评估工具（PROBAST）显示，十项研究的总体偏倚风险较低，但仅有一项研究纳入了临床参数（如年龄和格里森评分）。人工智能在识别淋巴结受累和转移性疾病方面表现出较高的准确性（98%），但敏感性存在差异（62%-97%）。其优势包括区分骨病变、评估肿瘤负荷、预测治疗反应以及实现任务自动化处理。总之，人工智能在提升PSMA PET扫描对mPCa的诊断潜力方面展现出良好前景，能够有效应对当前在效率和一致性方面的局限性。
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