人工智能在前列腺MRI中的应用：快速范围综述揭示筛查场景证据有限
该快速范围综述按PRISMA-ScR和Cochrane快速综述方法检索，最终仅2项研究符合纳入标准，均评估商业化的ProstateAI软件解读前列腺MRI。AI与专家放射科医师的一致性为差至中等（kappa 0.17-0.42），AI呈现高过度检测倾向和低特异度，导致与专家放射科医师不一致。作者结论为当前证据有限，AI需在真实筛查人群中更多训练和测试才能安全用于前列腺癌筛查。
要点
- 筛查场景下AI解读前列腺MRI的证据极少，仅2项研究符合纳入标准。
- AI与专家放射科医师一致性差至中等（kappa 0.17-0.42）。
- AI过度检测倾向高、特异度低，与专家放射科医师不一致。
核心结论
- 当前关于AI在前列腺MRI解读中应用的证据有限，AI与专家放射科医师一致性差至中等，过度检测倾向高且特异度低。
研究设计与样本
- 快速范围综述，遵循PRISMA-ScR指南和Cochrane快速综述方法，系统检索主要数据库并补充灰色文献，不限研究设计和时间。
- 共识别284条记录，47项研究评估合格性，最终仅2项研究符合纳入标准，均评估商业化ProstateAI软件工具解读前列腺MRI。
诊断标准与关键数值
- AI深度学习算法与专家放射科医师之间的一致性为差至中等，kappa值范围0.17-0.42。
- AI表现出高过度检测倾向和低特异度，导致与专家放射科医师判读不一致。
临床应用与阅片要点
- 在筛查场景中直接使用经医院转诊患者训练和测试的AI工具可能不适用于无症状男性MRI判读。
- 阅片时需警惕AI过度检测和低特异度带来的假阳性风险，AI结果应与专家判读结合评估。
局限性
- 符合纳入标准的研究仅2项，证据基础非常有限。
- 现有AI工具训练和测试人群为医院转诊患者，可能不适用于筛查人群。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) is seen as a potential solution to alleviate workforce demands arising from growing use of magnetic resonance imaging (MRI) in prostate cancer (PCa) screening. We aimed to synthesize the evidence on use of AI in prostate MRI readings in asymptomatic men in PCa screening settings.
METHODS: We conducted a rapid scoping review following PRISMA-ScR guidelines and Cochrane rapid review methods performing the systematic search of major databases supplemented by grey literature search with no restrictions in study design and time-duration. We considered various aspects of utilization of AI in MRI interpretations and biopsy indications in the screening setting. Anticipating limited evidence on AI implementation in screening settings, we extended the review from 'what is known' to discussion on 'key considerations for expected expansion'.
KEY FINDINGS AND LIMITATIONS: We identified 284 records with 47 studies assessed for eligibility and two studied met the inclusion criteria. Both evaluated commercially available ProstateAI software tool to interpret prostate MRI. Agreement between deep learning-based algorithm of AI and expert radiologist ranged from poor to moderate (kappa 0.17-0.42). AI demonstrated high tendency of over-detection and low specificity, leading to discordance with expert radiologists.
CONCLUSIONS AND CLINICAL IMPLICATIONS: Current evidence on use of AI in prostate MRI interpretation is limited, but this review highlights several important directions for future research and implementation. Generating robust evidence base in the coming years will be crucial to ensure that AI integration enhances the effectiveness and acceptability of future prostate cancer screening programs.
PATIENT SUMMARY: In this study, we examined whether Artificial Intelligence (AI) tools can accurately read prostate MRI scans of apparently healthy men for early detection of prostate cancer. We found that deploying current AI tools that are trained and tested in hospital referred patients may not be optimal to read MRI performed in asymptomatic men. We conclude that AI needs much more training and testing in real screening populations before it can be safely used in prostate cancer screening programs.
引用
- Singh D, Salazar Gutiérrez JP, Rouviere O, et al. Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context. Eur J Radiol. 2026. PMID: 42142521. doi:10.1016/j.ejrad.2026.112930
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。