Detecting lung cancer effectively in the general population is essential for optimizing treatment outcomes and improving the 5-year survival rate. While low-dose computed tomography (LDCT) is the current standard, it has limitations in broader populations. We developed a blood-based multi-omics model using whole-genome cell-free DNA (cfDNA) features to distinguish lung cancer from non-cancer individuals. This study included 1600 patients and an equal number of non-cancer controls, divided into training and validation cohorts. The model achieved an area under the curve (AUC) of 95.59% for the training cohort and 95.74% for the validation cohort. The model consistently performed well across various cancer stages and histological subtypes. To further validate the performance of the model, an external validation cohort was utilized. Notably, it also effectively differentiated non-cancer samples from cancer samples in the external validation cohort, with 85.9% sensitivity and 94.78% specificity. Importantly, in simulated population screenings, our ctDNA assay outperformed both LDCT and a previously established method. This suggests its potential utility in wider lung cancer screening programs, possibly complementing the LDCT approach. In conclusion, our ctDNA assay emerges as a promising and highly sensitive tool for the early detection and categorization of lung cancer.
He et al. (2026) studied this question.