Transforming Lung Cancer Management with AI and Biomarkers: From Early Detection to Targeted Therapies

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) representing the primary histological subtypes. Approximately 85% of cases are classified as NSCLC, whereas SCLC is characterized by its aggressive clinical course and poor prognosis. Major risk factors for SCLC include smoking, environmental exposures, and genetic predispositions. Early detection is critical; low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer–specific mortality. The use of biomarkers, such as microRNAs (miRNAs), has enhanced diagnostic accuracy by improving risk stratification and distinguishing malignant from benign pulmonary nodules. Artificial intelligence (AI)-driven imaging technologies have revolutionized lung cancer screening by increasing diagnostic precision and operational efficiency. Advances in molecular characterization, including identification of mutations and gene fusions involving EGFR, KRAS, ALK, and NTRK, have facilitated the development of personalized therapeutic strategies. In particular, immunotherapies and targeted agents, such as TRK inhibitors, have demonstrated promising efficacy in NSCLC. Nevertheless, significant challenges remain, including therapy resistance, healthcare disparities, and limited accessibility to screening programs. Emerging technologies, such as liquid biopsies and deep learning–enhanced imaging, continue to drive improvements in early detection and treatment paradigms. Future research should prioritize the integration of AI, novel therapeutics, and precision medicine approaches to optimize lung cancer management. By advancing early diagnostic capabilities, expanding personalized interventions, and addressing disparities in access to care, meaningful progress can be achieved in reducing lung cancer mortality.