A Case Series From a Multicentric Study: Can Artificial Intelligence (AI)-Enabled Chest X-Ray Assist in the Incidental Detection of Early-Stage Lung Cancers?

Lung cancer remains the leading cause of cancer -related mortality, largely driven by late stage diagnosis when curative treatment options are limited. Although chest X-ray (CXR) is the most widely used imaging modality globally, pulmonary nodules on CXR are frequently missed due to their subtle presentation and high radiology workloads. This case series highlights the role of AI-enabled CXR analysis using qXR-LNMS in enabling opportunistic early detection.

All consecutive CXRs at participating centers were analyzed using qXR-LNMS, with high-risk cases flagged for clinical review and further investigation. Five asymptomatic patients, who underwent imaging for non-respiratory indications, were identified through this pathway. None had prior suspicion or symptoms of lung cancer. AI flagged high-risk nodules, prompting follow-up with CT imaging and biopsy, which confirmed early-stage lung cancer in all cases. Four patients underwent curative surgery with favorable outcomes, while one received chemotherapy. 

Additional cases from Hacettepe University, Turkey, demonstrated similar outcomes, where AI identified unsuspected nodules leading to early diagnosis of Lung Cancer and successful surgical intervention. These findings are supported by data from the CREATE study, which validates the ability of AI to stratify malignancy risk in incidental nodules.

This study demonstrates that AI can act as an opportunistic screening layer, identifying high-risk nodules in asymptomatic individuals outside traditional screening criteria. Integrating AI into routine workflows that offers a scalable, cost-effective approach to expand early lung cancer detection, particularly in settings without formal screening programs.