Lung cancer persists as the predominant oncological cause of mortality globally, underscoring an imperative public health issue that demands effective screening methodologies to mitigate its impact. The National Lung Screening Trial (NLST) from the National Cancer Institute has established that low-dose computed tomography (LDCT) can detect lung cancer at an early stage and decrease mortality. Nonetheless, concerns such as radiation-induced risks, false positives, overdiagnosis, and medical costs demand attention. The importance of Artificial Intelligence (AI) in lung cancer screening is growing due to its superior capabilities for extracting image data and managing complex models. Circulating tumor markers (CTMs), encompassing circulating tumor DNA (ctDNA), circulating tumor RNA (ctRNA), circulating tumor cells (CTCs), and exosomes, present a non-invasive diagnostic and surveillance strategy for lung cancer. Despite their established utility in treatment and prognostic monitoring, the application of CTMs in early lung cancer screening is less documented. However, recent innovations highlight the potential of AI in conjunction with CTMs to enhance early diagnostic capabilities. This review synthesizes current research on the convergence of AI with CTMs, offering innovative avenues to augment and refine lung cancer screening methodologies.
Li et al. (Thu,) studied this question.
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