Integrating artificial intelligence with circulating tumor DNA for non-small cell lung cancer: opportunities, challenges, and future directions - Thalambedu, Nishanth; Balla, Mamtha; Sivasubramanian, Barath Prashanth; Sadaram, Prasanth; Malla, Krishna Prathiba; Vasipalli, Krishna P.; Kakadia, Sunil (Frontiers in Medicine, 2025)
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer mortality, with late-stage diagnosis contributing to poor survival. Circulating tumor DNA (ctDNA) has emerged as a non-invasive biomarker for screening, diagnosis, and monitoring, with limitations about sensitivity and specificity challenges. The integration of artificial intelligence (AI) offers a promising avenue to enhance ctDNA applications in NSCLC by improving mutation detection rates and sensitivities, refining minimal residual disease (MRD) predictions, enabling earlier detection of relapse, sometimes earlier than imaging, differentiating tumor vs. non-tumor derived signals to improve specificities. AI achieves 0.002% mutant allelic fraction detection, 94% relapse detection sensitivity, and 5.2-month lead time over imaging. This narrative review explores the role of ctDNA in NSCLC management, highlighting how AI amplifies its utility across screening, diagnosis, treatment evaluation, MRD detection, and disease surveillance while outlining key opportunities, challenges, and future directions.