The technology was able to identify specific genetic changes with high accuracy, offering a potentially faster, more efficient and cheaper testing option than traditional methods.
The findings have the potential to accelerate testing for lung cancer patients, helping doctors to identify the right treatment for patients more quickly, experts say.
Genetic changes
Lung cancer remains the leading cause of cancer-related death worldwide. Some lung cancers carry specific DNA genetic changes, such as mutations in the EGFR gene, which can determine whether patients would benefit from targeted treatments.
Detecting these mutations currently requires laboratory tests like gene sequencing, which can be expensive, time-consuming, and use up valuable tissue from small biopsy samples. Availability of tissue is often limited, so there is a need for non-invasive approaches to identify EGFR mutations.
Light signals
Researchers from the University of Edinburgh and NHS Lothian have developed a new approach using a technique called fluorescence lifetime imaging microscopy (FLIM) to predict EGFR mutations without the need for genetic testing or tissue staining.
The technology captures natural light signals from tissue samples, which are then analysed by artificial intelligence for patterns.
In the study, the method was able to predict the presence of EGFR mutations with very high accuracy. It could also distinguish between the two most common types of EGFR mutations that are important for treatment decisions.
Preserve tissue
Expanded lung cancer screening programmes are increasingly detecting suspected cancers at an earlier stage, placing pressure on diagnostic pathways to deliver fast, accurate results from limited tissue samples.
Experts say the new approach has the ability to speed up diagnosis, as well as preserving limited biopsy material – the method uses untreated tissue, leaving it intact and available for further analysis.