Journal: Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
This review explains how lung cancer diagnostics are shifting from purely anatomic and histologic methods to integrated molecular and computational approaches.
Key points:
- Limitations of traditional diagnostics: X‑ray, CT, bronchoscopy, and tissue biopsy often miss early-stage disease or lack sufficient detail for optimized treatment selection.
- Molecular biomarkers in routine practice: Incorporation of EGFR, ALK, KRAS, BRAF, MET, and PD‑L1 into standard work‑ups enables more precise subtyping and targeted/immunotherapy selection.
- Liquid biopsy and circulating tumor DNA: These methods offer noninvasive ways to characterize tumors and monitor disease and treatment response in real time, complementing or sometimes reducing reliance on repeat tissue biopsies.
- Next‑generation sequencing and multi‑omics: Genomic, transcriptomic, and proteomic profiling provide a more comprehensive view of tumor biology and the tumor microenvironment, improving identification of actionable alterations and resistance mechanisms.
- Imaging, radiomics, and AI: Advanced image analysis, including radiomics and pattern recognition with machine learning and deep learning, enhances the interpretation of low‑dose CT screening, aiding early lesion detection and risk stratification.
- AI‑driven clinical decision support: AI-powered computer-aided detection and predictive models are beginning to assist clinicians in diagnosis and treatment planning, moving toward more personalized care.
- Challenges and caveats: The review stresses ongoing issues in data standardization, model interpretability, robust clinical validation, and ethical concerns (e.g., privacy, bias). The authors view the convergence of digital technologies and molecular biology as promising but still evolving, with the potential to deliver faster, more precise, and more patient-specific lung cancer diagnosis.