Early-stage lung cancer detection via thin-section low-dose CT reconstruction combined with AI in non-high risk population: a large-scale real-world retrospective cohort study

Abstract Background Current Lung cancer screening guidelines recommend annual low-dose computed tomography (LDCT) for high-risk individuals. However, the effectiveness of LDCT in non-high risk individual remains inadequately explored. With the incidence of lung cancer steadily increasing among non-high risk individuals, this study aims to assess the risk of lung cancer in non-high risk individuals and evaluate the potential of thin-section LDCT reconstruction combined with artificial intelligence (LDCT-TRAI) as a screening tool. Methods A real-world cohort study on lung cancer screening was conducted at West China Hospital of Sichuan University from January 2010 to July 2021. Participants were screened using either LDCT-TRAI or traditional LDCT. The AI system employed was the uAI-ChestCare software. Lung cancer diagnoses were confirmed through pathological examination. Results Among the 259 121 enrolled non-high risk participants, 87 260 (33.7%) had positive screening results. Within one year, 728 (0.3%) participants were diagnosed with lung cancer, of whom 87.1% (634/728) were never-smokers, and 92.7% (675/728) presented with stage I disease. Compared with traditional LDCT, LDCT-TRAI demonstrated a higher lung cancer detection rate (0.3% vs. 0.2%, P < 0.001), particularly for stage I cancers (94.4% vs. 83.2%, P < 0.001), and was associated with improved survival outcomes (5-year OS rate: 95.4% vs. 81.3%, P < 0.0001). Conclusion These findings highlight the importance of expanding lung cancer screening to non-high risk populations, especially never-smokers. LDCT-TRAI outperformed traditional LDCT in detecting early-stage cancers and improving survival outcomes, underscoring its potential as a more effective screening tool for early lung cancer detection in this population.