Early diagnosis of lung cancer using magnetic nanoparticles-integrated systems

Abstract

Lung cancer (LC) has high morbidity and fatality rate that can be attributed to its poor diagnostic and monitoring facilities. Hence, there is a need to design advanced detection and monitoring systems to facilitate fast, efficient, and early diagnosis. The emerging research on novel nanotechnology-based strategies and conceptual models has made early-stage detection of LC possible by employing magnetic nanoparticles (MNPs) to surmount the barriers of slow diagnostic efficiency. Herein, the emphasis is on the recent advancement of MNP-based detection and monitoring systems for LC diagnosis, and future perspectives in the current scenario are discussed. The integration of MNP-based advanced diagnostic tools (microfluidic chips, artificial intelligence, biosensors, biomarkers detection, machine learning, nanotheranostics, deep learning, and internet of things platform) with conventional ones bronchoscopy, computed tomography scan, positron emission tomography, distant metastases, transthoracic biopsy, and magnetic resonance imaging might help to resolve current challenges related to early diagnosis of LC.