Integrative approaches in lung cancer diagnosis: bridging molecular biomarkers and AI driven imaging

Early and accurate detection of lung cancer remains a major clinical challenge. Conventional diagnostics, including X-ray, tissue biopsy etc., have limited sensitivity for identifying tumors early. Recent advances in molecular biology and computational technologies have significantly transformed lung cancer diagnostics.

This review examines recent developments in biomarker-driven and technology-assisted diagnostic strategies for lung cancer. It highlights clinical relevance of molecular biomarkers, including EGFR, ALK etc., and evaluates emerging approaches like next-generation sequencing (NGS), ctDNA analysis, AI-based analytical tools.

Integration of molecular biomarkers into routine diagnostics has improved tumor subtyping and enabled more targeted therapeutic selection. Non-invasive approaches like liquid biopsy facilitate real-time tumor characterization and disease monitoring. In parallel, NGS and multi-omics technologies like genomics, transcriptomics provide comprehensive insights into tumor biology and tumor microenvironment. Advances in radiomics and AI-driven image analysis, particularly machine learning and deep learning applied to low-dose CT imaging, enhancing early detection and risk stratification. AI-powered detection systems and predictive models further support clinical decision-making.

The convergence of biomarker research, multi-omics technologies, and AI-driven analytics is reshaping lung cancer diagnostics toward more precise and personalized approaches. However, challenges related to data standardization, interpretability, clinical validation, and ethical considerations must be addressed to enable widespread clinical implementation.