评估人工智能辅助的前列腺双参数MRI解释:国际多读者研究
David G Gelikman1, Enis C Yilmaz1, Stephanie A Harmon1
1Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
AJR. American journal of roentgenology
|July 16, 2025
概括
人工智能 (AI) 通过增加积极的预测值和读者同意来改善双参数MRI (bpMRI) 上的前列腺癌检测,尽管灵敏度略有下降. 这种人工智能工具显示出对更一致的bpMRI解释的希望.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 前列腺癌的诊断方法 前列腺癌的诊断方法
背景情况:
- 前列腺双参数MRI (bpMRI) 解释的变化影响前列腺癌 (PCa) 的诊断可靠性.
- 人工智能 (AI) 提供了标准化解释和提高bpmri诊断准确性的潜力.
研究的目的:
- 评估深度学习人工智能模型对临床显著PCa (csPCa) 和PCa的病变和患者水平检测率的影响.
- 评估人工智能对bpmri解释中的读者间协议的影响.
主要方法:
- 追溯式,多读者,多中心研究,涉及180名接受bpMRI的患者.
- 六名放射科医生解释了bpMRI扫描,有和没有人工智能协助.
- 参考标准包括整体病理学或系统活检;评估了病变水平的敏感性,PPV,患者水平的AUC和读者之间的协议.
主要成果:
- 人工智能显著改善了csPCa和PCa检测的病变水平正预测值 (PPV) (p < .001).
- 人工智能辅助导致病变水平敏感度略有降低 (p = .01),但在患者水平AUC上没有显著差异.
- 在AI的帮助下,PI-RADS分数 (损伤和患者水平) 和损伤大小测量显著改善 (p < .001).
结论:
- 在bpMRI解释中集成人工智能可以增强病变级别的PPV和读者之间的协议.
- 虽然灵敏度略有下降,但AI显示出提高一致性和减少PCa诊断中的错误阳性潜力.
- 需要进一步优化人工智能模型,以平衡灵敏度和特异性,以改善临床影响.
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