Integrative Approaches in Lung Cancer Diagnosis: Bridging Molecular Biomarkers and AI Driven Imaging - PubMed
3 hours ago
- #Lung Cancer
- #Precision Medicine
- #Artificial Intelligence
- Traditional diagnostic methods like X-ray, CT scans, and biopsies often fail to detect lung cancer early.
- Recent advances integrate molecular biomarkers (EGFR, ALK, KRAS, BRAF, MET, PD-L1) for precise subtyping and therapy selection.
- Liquid biopsy and circulating tumor DNA offer noninvasive ways to monitor tumors in real-time.
- Next-generation sequencing and multiomic approaches (genomics, transcriptomics, proteomics) provide detailed tumor microenvironment profiles.
- AI and radiomics enhance early lesion detection on low-dose CT scans, improving risk stratification.
- AI-powered predictive models and computer-aided detection systems support clinical decision-making for personalized diagnostics.
- Challenges remain in data standardization, interpretability, clinical validation, and ethical considerations.
- The convergence of digital innovation and biological insights promises faster, more precise lung cancer diagnosis.