与英国胸腔学会关于常规计算机断层扫描 (CT) 的指导方针相比,CADx工具改善了肺结节风险分层
L Duerden1, B A Krajina2, M E Calhoun2
1Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, UK.
Clinical radiology
|November 14, 2025
概括
一个人工智能 (AI) 工具改善了肺结节的分类,将延迟的癌症诊断减少了20%,并优化了对良性肺结节的随访. 这种AI分类器在与英国胸腔学会 (BTS) 准则一起使用时可以提高决策.
科学领域:
- 肺部病理学 肺部病理学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 偶然的肺结节是计算机断层扫描 (CT) 扫描的常见发现.
- 目前的指导方针,如英国胸腔学会 (BTS) 2015年指导方针,指导这些结节的管理.
- 准确的风险分类对于及时诊断和适当的患者管理至关重要.
研究的目的:
- 评估基于CT图像的肺结节人工智能 (AI) 分类器是否可以改善风险分类.
- 评估人工智能是否与BTS 2015指南相比提高了临床管理决策.
- 确定人工智能对减少延迟诊断和优化后续行动的影响.
主要方法:
- 一项回顾性病例对照研究,使用来自英国国家卫生服务 (NHS) 人口的CT扫描.
- 一个人工智能工具 (RevealAI-Lung) 计算了肺结节的恶性瘤相似性指数 (mSI).
- 用mSI调整对BTS 2015建议进行重新分类并进行比较.
主要成果:
- 人工智能重新分类将延迟癌症诊断从45%降低到25% (P<0.001),通过升级44%的相关病例.
- 人工智能降低了7%的良性结节,可能避免了不必要的后续扫描.
- 人工智能升级了7%的良性结节从间隔扫描到即时正子发射断层扫描 (PET) -CT.
结论:
- 人工智能分类器显著改善了偶然发现的肺结节分类.
- 将AI与BTS指导方针结合起来,可以提高肺结节的临床决策.
- 人工智能显示出优化诊断途径和减少医疗保健负担的潜力.
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