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使用人工智能作为风险预测模型,在患有不明确多参数前列腺MRI发现的患者中使用人工智能

Abdullah Al-Khanaty^1,2, David Hennes^1,3, Arjun Guduguntla²

¹Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia.

|January 10, 2026

概括

人工智能 (AI) 可以改善PI-RADS 3病变中临床显著前列腺癌 (csPCa) 的检测,减少不必要的活检. 人工智能模型在与放射科医生的表现相匹配或超过方面表现有希望,有助于更好地管理患者.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能的人工智能

背景情况:

在多参数MRI (mpMRI) 上的PI-RADS 3病变是一个诊断挑战,临床上显著的前列腺癌 (csPCa) 的检测率很低.
这种诊断不确定性导致不必要的活检和错过的csPCa诊断.
人工智能 (AI) 为这些不确定的发现提供了客观和可重现的风险分层的潜在解决方案.

研究的目的:

对PI-RADS 3病变中风险分层的AI应用的现有证据进行审查.
澄清像PI-CAI研究这样的多中心倡议在对AI与专家放射科医生进行基准测试中的作用.

主要方法:

对PubMed和Embase数据库进行了叙事审查.
包括评估AI在PI-RADS 3病变中对csPCa预测的研究,使用活检或随访作为参考标准.
强调的是外部验证的AI模型.

主要成果:

放射学和深度学习人工智能模型展示了在PI-RADS 3病变中区分csPCa和良性组织的能力.
人工智能有可能将良性活检率降低30-40%,并改善csPCa检测.
该PI-CAI研究表明,人工智能系统可以在不同环境中在csPCa检测方面与专家放射科医生匹配或超过.

结论:

人工智能具有显著的潜力,可以通过减少不必要的活检和提高csPCa检测来改善PI-RADS 3病变的管理.
进一步的前性,多中心验证和整合到临床工作流程中是必要的,以实现例行采用.
协调成像协议和明确的决策支持对于成功实施人工智能至关重要.

关键词:

皮-拉德斯3 皮拉德斯3 人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习是机器学习.多参数核磁共振成像前列腺癌是前列腺癌.无线电学 (radiomics) 是一种无线电学.

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