Artificial intelligence-driven methods and nanoparticle-based biosensors for rapid and sensitive detection of EGFR mutations in non-small cell lung cancer

Abstract

Abstract More than 85% of lung cancer cases around the world are non-small cell lung cancer (NSCLC). Epidermal growth factor receptor (EGFR) mutations are central to the initiation and progression of NSCLC, as well as to treatment response. As such, rapid and reliable detection of EGFR mutations, particularly at early stages of disease, can play a key role in determining patient management strategies. Nonetheless, the current gold-standard EGFR mutation detection methodologies using polymerase chain reaction (PCR) and DNA sequencing are expensive, time-consuming, and technically complex for convenient point-of-care decision-making and serial monitoring. In this review, we summarize recent advances in NP-based biosensors for EGFR mutations, with a focus on circulating tumor DNA (liquid biopsy). Emphasis is placed on biosensing approaches based on gold nanoparticles (AuNPs), quantum dots, magnetic NPs, mesoporous silica, and other nanocomposites. We further review their operating principles, analytical performance (LOD and response time), reproducibility, and stability in biological solutions. In addition, advanced intelligent technologies, including artificial intelligence (AI), are introduced into biosensor processing to enhance signal amplification and multiplexed readouts. Furthermore, the principal obstacles to their clinical translation, such as standardization, validation, and regulatory approval, are also addressed. Finally, we outline future opportunities, including multiplexed sensing platforms for simultaneous detection of actionable oncogenic drivers, wearable/biosensors for point-of-care detection, and AI-integrated workflows.