Lung cancer is still the leading cause of cancer-related death worldwide, largely due to smoking and environmental exposure. It mainly includes non-small cell lung cancer, which makes up most cases and has a low long-term survival rate, and small cell lung cancer, which is less common but more aggressive. Current diagnosis relies heavily on CT and PET imaging and a few blood markers, but these methods often produce false positives, involve radiation exposure, and lack accuracy because many markers are also elevated in non-cancer conditions.
To address these limits, research is moving toward advanced molecular biomarkers and liquid biopsies, such as circulating tumor DNA and exosomes. These approaches aim to detect cancer more precisely and guide targeted treatments. New genetic targets, including HER2, PIK3CA, BRCA1/2, and NRG1, are becoming increasingly important, even though many are not yet used routinely. At the same time, organizations like the FDA and NIH are working to standardize biomarker use and clarify their clinical value.
Artificial intelligence plays a growing role by analyzing complex biological data, uncovering patterns that traditional methods may miss, and supporting personalized treatment decisions. However, many promising biomarkers never make it from research into clinical practice, and ethical concerns remain around data quality, interpretation, and genetic testing. Overall, combining molecular biomarkers, liquid biopsies, and AI offers strong potential to improve early detection and personalize lung cancer treatment, despite ongoing challenges.