Advances in molecular imaging for early detection of lung cancer

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.

Authors

Publication Details

Journal
Advances in Radiotherapy & Nuclear Medicine
Published
2024-09-19
DOI
https://doi.org/10.36922/arnm.4173
Citations
5
Primary Topic
Atomic and Subatomic Physics Research
Type
article
Field-Weighted Citation Impact
1.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advances in molecular imaging for early detection of lung cancer

Dongjun Li, Jinghui Peng, Bamishaye Oluwabukola, Mimba Brenda-Ruth
5 citations
Advances in Radiotherapy & Nuclear Medicine
Atomic and Subatomic Physics Research
1.69
article

Advances in molecular imaging for early detection of lung cancer

Dongjun Li, Jinghui Peng, Bamishaye Oluwabukola, Mimba Brenda-Ruth
article en
5 citations

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.

Advances in Radiotherapy & Nuclear MedicineVol. 2(3)
Openalex Percentile: Top 16%
Atomic and Subatomic Physics Research
1.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.