Specific PET Imaging for Precision Management of Lung Cancer: Advances, Clinical Translation and Future Directions

Lung cancer continues to be the leading cause of cancer-related deaths worldwide, primarily due to persistent challenges in early detection and the limited effectiveness of precision medicine. Although low-dose computed tomography (CT) has been widely implemented for lung cancer screening and has contributed to a measurable reduction in disease-specific mortality, its diagnostic accuracy is limited by its inability to reliably distinguish benign from malignant pulmonary nodules. Furthermore, the clinical standard for metabolic imaging¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)is significantly constrained by a high incidence of false-positive findings in patients with inflammatory conditions and frequent false-negative results in tumors exhibiting low glycolytic activity. To overcome these critical limitations, highly specific PET radiotracers have been developed to target immune checkpoints, cell surface receptors, and distinct features of the tumor microenvironment. These molecularly targeted probes offer improved biological specificity, enabling more precise visualization of tumor pathophysiology and supporting applications in early diagnosis, real-time treatment monitoring, and prognostic stratification. This review summarizes recent advances in targeted PET imaging and critically evaluates the potential of integrating these approaches with multimodal imaging, liquid biopsy, and artificial intelligence (AI)-driven radiomics to enhance diagnostic accuracy and inform therapeutic decision-making. By synthesizing key developments from both preclinical studies and clinical trials, we highlight the translational significance and future directions of target-specific PET tracers. Despite existing challenges related to tracer development, regulatory approval, and methodological standardization, the integration of next-generation whole-body PET systems with advanced artificial intelligence algorithms holds considerable promise for establishing molecular-targeted PET as a cornerstone of precision oncology in lung cancer management.