A new study, published in Cancer Research, a journal of the American Association for Cancer Research, develops a new method that can accurately predict specific gene changes that cause lung cancer without the need for additional expensive lab techniques. This work, co-led by IRR’s Prof Ahsan Akram and Dr Qiang Wang, has the potential to speed up testing for lung cancer patients who could benefit from targeted treatments. Fluorescence lifetime images of lung cancer samples, demonstrating the natural fluorescence signals emitted from the cancer which are used to predict mutations. 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. Identifying these genetic changes is important because they determine whether patients can benefit from targeted treatments.Currently, detecting these mutations requires laboratory tests like gene sequencing, which can be expensive, time-consuming, and use up valuable tissue from small biopsy samples. Availability of sufficient tissue is often a limitation to these tests, so there is a need for non-invasive approaches to identify EGFR mutations.With the expansion of lung cancer screening programmes, clinicians are increasingly detecting suspected cancers at an earlier stage, placing new pressure on diagnostic pathways to deliver fast, accurate results from limited tissue samples.In this study, IRR researchers together with colleagues from the Royal Infirmary Edinburgh, used a new approach they’ve recently developed to predict EGFR mutations without needing traditional genetic testing or tissue staining. They used a special imaging technique that captures natural light signals from untreated tissue samples, combined with artificial intelligence (AI) to analyse these patterns.The researchers’ new 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. This is a significant step towards a future where a single, non-destructive fluorescence scan of a biopsy could quickly inform clinicians whether a patient is likely to respond to targeted treatment- ensuring the right treatment reaches the right patient more quickly. Professor Ahsan Akram IRR Group Leader and paper’s co-lead Not only does this approach speed up diagnosis, it also preserves limited biopsy material and may reduce the need for complex laboratory procedures. In the future, this could lead to faster, more efficient, and less invasive testing for lung cancer patients. As lung cancer screening programmes expand, we will be dealing with more patients, earlier-stage disease, and more biopsy samples. The pressure on diagnostic services will be significant. Technologies like this, that can do more with less tissue and faster, will be essential for clinically effective diagnostic pathways. Dr David Dorward Consultant Thoracic Pathologist, NHS Lothian This approach has the potential to take processes that currently cost thousands of pounds and require weeks of lab work, and reduce them to something that takes minutes and costs hundreds. That is a step change in what is clinically achievable, particularly for centres and health systems where access to complex molecular testing is limited. Dr Qiang Wang IRR Group Leader and paper’s co-lead The research team is now working towards clinical validation of these approaches, with further work aiming to extend this platform to other cancer types, additional targetable mutations, and integration into clinical workflows.This work was supported by University of Edinburgh, NVIDIA Academic Hardware, Pathological Society and UKRI.Related linksRead the full article in American Association for Cancer ResearchRelated paper: New method can diagnose lung cancer tissue quickerAkram research group Tags CIR This article was published on Thursday 25 June 2026
