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REVIEW

Advances in molecular imaging for early detection of lung cancer

Dongjun Li1* ,  Mimba Brenda-Ruth1 ,  Bamishaye Oluwabukola1 ,  Jinghui Peng2
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1 Department of Chemistry, Georgia State University, Atlanta, Georgia, United States of America
2 Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Hubei, Wuhan, China
ARNM 2024, 2(3), 4173 https://doi.org/10.36922/arnm.4173
Submitted: 9 July 2024 | Accepted: 6 August 2024 | Published: 19 September 2024
© 2024 by the Author (s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Lung cancer remains the second most commonly diagnosed cancer globally and the leading cause of cancer-related deaths, a trend consistent in the United States as of 2023. One of the key reasons for the high mortality rate of lung cancer is its poor prognosis, with 75% of patients diagnosed at middle and advanced stages. Early detection of subclinical lung cancer, metastases, and their fibrotic stroma is crucial for enabling timely treatment, reducing reoccurrence, and stratifying patients. Current diagnostic methods, such as lung biopsy for patients with small nodules, are highly invasive and technically challenging. The radiological gold standard, computed tomography (CT), is associated with ionizing radiation. However, positron emission tomography (PET) and magnetic resonance imaging (MRI) have emerged as promising methodologies for lung cancer diagnosis. PET tracers with a variety of targeting mechanisms are currently under development in human trials. With advancements in hardware and software over the past decades, radiation-free MRI has been clinically and preclinically validated as an alternative to CT. Moreover, novel-targeted MRI contrast agents have been tested in animal models and show strong translational potential. In this review, we summarize the state-of-the-art progress in molecular imaging for the early detection of lung cancer and its potential biomarkers.

Keywords
Early detection
Lung cancer
Molecular imaging
Magnetic resonance imaging
Positron emission tomography
Computed tomography
Funding
Dongjun Li was sponsored by GSU MBD fellowships, while Bamishaye Oluwabukola was sponsored by GSU CDT fellowships.
Conflict of interest
The authors declare no conflicts of interest.
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Advances in Radiotherapy & Nuclear Medicine, Electronic ISSN: 2972-4392 Published by AccScience Publishing
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