Advanced multimodal diagnostic approaches for detection of lung cancer

ABSTRACT

Introduction: Lung cancer (LC) emerges as a principle cause of death among smokers and is also one of the most lethal forms of cancer in nonsmokers. LC is mainly classified as non-small cell lung cancer (NSCLC), small cell LC, and lung carcinoid tumor. NSCLC is the most prevalent form of LC and its early stage diagnosis is essential to reduce mortality rate of patients and provide specific therapy. The field of LC diagnostics witnessed a gradual escalation with advancement in technology.

Areas covered: This comprehensive review focuses on classification of LC and advanced diagnostics for LC detection like biosensors, biomarkers, nanotechnology-based diagnostics, wearable devices, mobile health, artificial intelligence (AI), aptamers, and molecularly imprinted polymers (MIPs).

Expert opinion: Liquid biopsy and breath analysis developments are the most promising and advanced technologies for the detection of biomarkers associated with LC. Wearable devices and AI are two niche areas that require development and standardization for commercialization. The upcoming technology based on nanosystems includes robots, fibers, and particles for sensitive detection of LC. In the near future, nanotechnology-based theranostics, aptamers, and MIPs will emerge in early-stage diagnosis of LC.

KEYWORDS:

• Lung cancer
• artificial intelligence
• nanotechnology
• theranostics
• biosensors

Article highlights

• Early diagnosis of lung cancer is possible using advanced technology like biosensing and molecular markers.

• Aptamers and molecularly imprinted polymers are promising technology for sensitive detection of lung cancer.

• Artificial intelligence and wearable devices are emerging technology for accurate diagnosis and development of precision medicine.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewers’ disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Additional information

Funding

This paper was not funded.