An effective and affordable blood test for lung cancer early detection using four protein markers and artificial intelligence.

Background: Lung cancer constitutes the leading cause of cancer mortality globally. This study assessed LungCanSeek, a novel blood-based protein test for lung cancer early detection. Methods: This retrospective study enrolled 1,814 participants (1,095 lung cancer, 719 non-cancer) from three different cohorts. Blood samples were analyzed for four protein tumor markers (PTMs) using Roche cobas. Artificial intelligence (AI) algorithms were developed for lung cancer detection and subtype classification: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and small cell lung cancer (SCLC). A two-step approach was modeled, using LungCanSeek for initial screening, followed by low-dose computed tomography (LDCT) for LungCanSeek's positive cases. Results: LungCanSeek achieved 83.5% sensitivity, 90.3% specificity, and 86.2% accuracy overall. Sensitivities of LUAD, LUSC, and SCLC were 83.3%, 81.4%, and 91.9%. Sensitivity increased with clinical stage in non-small cell lung cancer (NSCLC): 59.5% (I), 69.8% (II), 86.5% (III), and 91.3% (IV). Sensitivities of limited-stage and extensive-stage SCLC were 91.3% and 93.0%, respectively. The subtype classification accuracy was 77.4%. Simulation model analysis showed that the two-step approach reduced 10.3-fold false positives and 2.5-fold cost compared to LDCT for lung cancer screening in high-risk population. Conclusions: LungCanSeek is a non-invasive and cost-effective test for lung cancer early detection. The two-step approach offers a cost-effective strategy for population-wide lung cancer screening.