Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening.
Authors
Affiliations (12)
Affiliations (12)
- Department of Public Health, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
- Institute for Diagnostic Accuracy, Groningen, The Netherlands.
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 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.
- National Heart and Lung Institute, Imperial College, London, United Kingdom.
- Department of Respiratory Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands.
- Institute for Diagnostic Accuracy, Groningen, The Netherlands. [email protected].
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands. [email protected].
Abstract
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.