肺癌筛查中纵向肺结节匹配的自动化人工智能性能
Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening.
作者
作者单位
- Department of Public Health, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
- Institute for Diagnostic Accuracy, Groningen, The Netherlands.
- Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom.
- Department of Radiology and Nuclear Medicine, Gelre Ziekenhuizen, Apeldoorn, The Netherlands.
- Department of Medicine and Surgery (DiMeC), Scienze Radiologiche, University of Parma, Parma, Italy.
- Coreline Soft, Seoul, Republic of Korea.
- Royal Brompton Hospital London, Chelsea, London, United Kingdom.
- Institute for Diagnostic Accuracy, Groningen, The Netherlands. m.oudkerk@rug.nl.
摘要
中文
准确的纵向结节匹配是肺癌筛查中自动化增长率（体积倍增时间）评估的关键技术前提。本研究评估了人工智能（AI）肺结节分析系统在361名接受3个月随访低剂量计算机断层扫描（LDCT）的英国肺癌筛查（UKLS）试验参与者中的表现。该肺部AI使用更新的体积阈值（按NELSON 2.0/EUPS方案，实性成分≥100 mm³）独立评估基线扫描，以确定需要3个月随访的病例。为评估算法的真实稳健性，所有AI检测到的基线候选结节（≥100 mm³）均在没有人工选择的情况下进行全自动纵向匹配。肺部AI识别出181名参与者共378个基线结节≥100 mm³。随访时，39个结节自然消退。对于339个持续存在的结节，肺部AI的匹配成功率为83.5%（283/339；95% CI: 79.2-87.1%）。对于基线单个候选结节的参与者（占队列的59.7%），匹配性能为91.8%（89/97）；对于结节数超过5个的参与者（占6.6%），匹配性能为72.8%（75/103）。对56/339（16.5%）个未匹配结果的专家审查显示，几乎所有均为非结节结构（91.1%，51/56），主要为胸膜斑块（46.4%，26/56）。因此，仅5个未匹配的离散实性结节（占339个持续存在的发现的1.5%；95% CI: 0.6-3.5%）需要人工干预。总之，该肺部AI表现出稳健的纵向匹配性能，具有显著减少后续人工跟踪工作量的潜力。关键点：问题：独立的AI纵向结节追踪能否提供足够的结节水平技术可靠性，以避免自动化肺癌筛查工作流程中的人工审查瓶颈？发现：肺部AI匹配了83.5%的持续结节（单发结节为91.8%，>5个结节为72.8%）；91.1%的失败为非结节性，仅1.5%需要人工纠正。临床相关性：肺部AI匹配可能减少肺癌筛查中的人工跟踪工作量，仅1.5%的持续结节需要人工纠正。在高结节负担的扫描中性能降低，需要在不同人群中开展前瞻性验证。
English
Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an artificial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSON 2.0/EUPS protocol) for cases requiring 3-month follow-up. To assess true algorithmic robustness, all AI-detected baseline candidate nodules (≥ 100 mm³) proceeded to fully automated longitudinal matching without any manual selection. The pulmonary AI identified 181 participants with 378 baseline nodules ≥ 100 mm³. In total, 39 nodules had naturally resolved at follow-up. The pulmonary AI achieved an 83.5% (283/339; 95% CI: 79.2-87.1%) matching success rate for 339 persisting nodules. Matching performance was 91.8% (89/97) for participants with a single baseline candidate nodule (59.7% of the cohort) and 72.8% (75/103) for participants with more than five nodules (6.6%). Expert review of the 56/339 (16.5%) unmatched findings showed that almost all were non-nodular structures (91.1%, 51/56), predominantly pleural plaques (46.4%, 26/56). Consequently, only five unmatched discrete solid nodules (1.5%; 95% CI: 0.6-3.5% of 339 persisting findings) required manual intervention. In conclusion, the pulmonary AI demonstrates robust longitudinal matching performance, with substantial potential for follow-up manual tracking workload reduction. KEY POINTS: Question Does standalone AI longitudinal nodule tracking provide sufficient nodule-level technical reliability to avoid manual review bottlenecks in an automated lung cancer screening workflow? Findings Pulmonary AI matched 83.5% of persisting nodules (91.8% for single nodules, 72.8% for > 5 nodules); 91.1% failures were non-nodular, with only 1.5% requiring manual correction. Clinical relevance Pulmonary AI matching can potentially reduce the manual tracking workload in lung cancer screening, with only 1.5% of persisting nodules requiring manual correction. Performance is reduced in scans with high nodule burden, and prospective validation in diverse populations is needed.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 6.0
- 新锐分区
- 2区