Related Experiment Video
Updated: Feb 21, 2026
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
Published on: October 13, 2023
Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The
Deniz Koksal1, Arunkumar Govindarajan2, Hari Kishan Gonuguntla3
1Department of Chest Diseases, Hacettepe University, Faculty of Medicine, Ankara, Turkey.
Objective:
To evaluate the effectiveness of the artificial intelligence-based qXR lung nodule malignancy score (qXR-LNMS) in detecting high-risk incidental pulmonary nodules (IPNs) on chest X-rays (CXRs).
Patients And Methods:
The CREATE (NCT05817110), a prospective, observational study for participants aged 35 years or older with IPN (size, ≥8 to ≤30 mm) on CXR, enrolled 712 participants (high-risk: 498 and low-risk: 214) between April 1, 2023, and December 31, 2024. Participants were flagged by the Food and Drug Administration-cleared qXR detection algorithm and confirmed by radiologists. Threshold for success was set at 20% for positive predictive value (PPV) and 70% for negative predictive value (NPV). The primary and secondary outcomes included PPV and NPV of qXR-LNMS against the risk of malignancy assessed by radiologists using low-dose computed tomography (LDCT) and binarized risk categories based on Lung-RADS score and Mayo Clinic model and PPVs and NPVs by clinicodemographic characteristics with 95% CIs using Wilson score method.
Results:
Overall, the PPV and the NPV of qXR-LNMS risk prediction against radiologists' assessment on LDCT were 54.2% (95% CI, 49.8-58.5) and 93.5% (95% CI, 89.3-96.1), respectively. The agreement between Mayo Clinic model and qXR-LNMS was observed in 70.6% participants (Spearman correlation, 0.247). Results across key subgroups were consistent with all PPV and NPV point estimates crossing the prespecified threshold.
Conclusion:
The results demonstrate the potential of qXR-LNMS in predicting benign and malignant IPN on CXR, thereby supporting lung cancer screening, particularly in resource-limited settings, although further validation is needed.
Trials Registration:
clinicaltrials.gov Identifier: NCT05817110.
