Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.
J, S., JY, K., S, C., & YJ, S. (2026). Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.. Journal of Korean medical science. https://doi.org/10.3346/jkms.2026.41.e208
J S, JY K, S C, YJ S. Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.. Journal of Korean medical science. 2026; doi: 10.3346/jkms.2026.41.e208
J S, JY K, S C, et al. Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.[J]. Journal of Korean medical science. 2026. DOI: 10.3346/jkms.2026.41.e208.
@article{j2026,
author = {Son J and Kim JY and Chang S and Suh YJ},
title = {Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.},
journal = {Journal of Korean medical science},
year = {2026},
doi = {10.3346/jkms.2026.41.e208},
note = {PMID: 42617189},
}
TY - JOUR AU - Son J AU - Kim JY AU - Chang S AU - Suh YJ TI - Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study. T2 - Journal of Korean medical science PY - 2026 DO - 10.3346/jkms.2026.41.e208 AN - PMID:42617189 ER -
BACKGROUND: To investigate the effectiveness of an artificial intelligence (AI)-based computer-aided detection (CAD) system in identifying incidental lung cancer on cardiac computed tomography (CT) scans and to compare its performance with that of radiologists. METHODS: In this retrospective, multicenter study, 652 cardiac CT scans from 581 patients subsequently diagnosed with lung cancer were analyzed. A commercial AI-CAD system was employed to detect pulmonary lesions on cardiac CT. The detection rate of AI-CAD was compared to that of the radiologist, based on the radiology report, as well as to the detection rate when combining AI-CAD and the radiologist. The characteristics of the lesions detected and missed by the radiologist and AI-CAD were compared. RESULTS: Radiologists and AI-CAD demonstrated similar detection rates for lung cancer (76.2% vs. 77.4%, P = 0.551). However, combining radiologists and AI-CAD significantly improved the detection rate to 90.4% (P < 0.001) compared to that of the radiologist alone. AI-CAD showed a higher detection rate in identifying small, peripheral, and part-solid lesions (all P < 0.001). Furthermore, AI-CAD outperformed radiologists in detecting limited-stage lung cancer (80.3% vs. 74.7%, P = 0.006). Among lung cancer cases missed by radiologists, 94.2% experienced diagnostic delays of > 100 days, with 78.2% leading to stage progression. AI-CAD identified 58.5% of these diagnostic delays. CONCLUSION: AI-CAD demonstrated the potential to improve the detection rate of incidental lung cancer by identifying a subset of lesions that were initially overlooked by radiologists on cardiac CT. It exhibited particular strength in identifying early-stage cancers and small, subsolid lesions.