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Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer death worldwide. Early detection of lung cancer can lead to identification of the cancer at its initial treatable stages and improves survival. Low-dose CT scan (LDCT) is currently the gold standard for lung cancer screening in high-risk individuals. Despite the observed stage migration and consistently demonstrated disease-specific overall survival benefit, LDCT has inherent limitations, including false-positive results, radiation exposure, and low compliance. Recently, new techniques have been investigated for early detection of lung cancer. Several studies have shown that liquid biopsy biomarkers such as circulating cell-free DNA (cfDNA), microRNA molecules (miRNA), circulating tumor cells (CTCs), tumor-derived exosomes (TDEs), and tumor-educated platelets (TEPs), as well as volatile organic compounds (VOCs), have the power to distinguish lung cancer patients from healthy subjects, offering potential for minimally invasive and non-invasive means of early cancer detection. Furthermore, recent studies have shown that the integration of artificial intelligence (AI) with clinical, imaging, and laboratory data has provided significant advancements and can offer potential solutions to some challenges related to early detection of lung cancer. Adopting AI-based multimodality strategies, such as multi-omics liquid biopsy and/or VOCs' detection, with LDCT augmented by advanced AI, could revolutionize early lung cancer screening by improving accuracy, efficiency, and personalization, especially when combined with patient clinical data. However, challenges remain in validating, standardizing, and integrating these approaches into clinical practice. In this review, we described these innovative milestones and methods, as well as their advantages and limitations in screening and early diagnosis of lung cancer.
Habbab et al. (Mon,) studied this question.