Abstract Lung cancer, the leading cause of global cancer‐related mortality, is categorized into small‐cell and non‐small‐cell subtypes. The heterogeneous non‐small‐cell lung cancer group is further subcategorized primarily into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, each underpinned by distinct molecular alterations. Although traditional serum biomarkers aid in subtype differentiation and treatment monitoring, their utility is limited by challenges such as poor specificity due to inflammatory confounders and the difficulty of dynamically tracking therapeutic resistance. Recent advances have identified emergent subtype‐specific biomarkers that reflect metabolic reprogramming, epigenetic dysregulation, stemness signatures, and interactions within the immune microenvironment. By integrating analytes such as ctDNA, exosomal RNAs, and urinary DNA with multi‐analyte panels and advanced imaging, liquid biopsies offer a promising avenue to enhance early detection accuracy, prognostication, and dynamic therapy monitoring. Nevertheless, the clinical adoption is hindered by several challenges, including incomplete validation, the need for technical standardization, intratumoral heterogeneity, and inter‐ethnic variability. The convergence of artificial intelligence (AI)‐enhanced multi‐omics with biomarker‐guided therapeutics represents a transformative strategy with the potential to overcome resistance, mitigate ethnic disparities, and ultimately transform lung cancer into a chronic, manageable disease. Therefore, prioritizing clinically validated AI‐integrated platforms is pivotal to achieve precision oncology.
Jin et al. (Fri,) studied this question.