Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification
Harriet L. Lancaster, Sunyi Zheng, Olga O. Aleshina, Donghoon Yu, Valeria, Yu. Chernina, Marjolein A. Heuvelmans, Geertruida H. de Bock, Monique D.Dorrius, Jan Willem Gratama, Sergey P. Morozov, Victor A. Gombolevskiy,
Mario Silva, Jaeyoun Yi, Matthijs Oudkerk
The study evaluated the performance of an AI-based lung cancer screening prototype, AVIEW LCS from Coreline Soft, compared to experienced radiologists in ultra-low-dose CT scans for lung cancer baseline screening. 283 participants were included, and the AI was able to automatically detect, measure, and classify solid nodules. Results showed that the AI outperformed radiologists in negative-misclassifications, indicating that AI could reduce radiologists' workload by up to 86.7%. The use of AI in lung cancer screening could also improve standardization and implementation. However, the study had limitations as it was focused on a specific screening setting with a highly standardized protocol, and its generalizability to other settings may be limited.