Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Drs Barbara Braden and Steve Taylor win funding to improve endoscopic detection of oesophageal cancer.

Clinical imaging is one approach that is being used to detect cancers earlier. However, progress in this field relies on the coming together of researchers from multiple disciplines, including engineering, physics, chemistry, mathematics, computer science and cancer biology. To this end, CRUK, the Engineering and Physical Sciences Research Council (EPSRC) and the Science and Technology Facilities Council (STFC) hosted a three day workshop in January 2019 to explore opportunities for optimising medical imaging in early detection.

During the course of the workshop, the multidisciplinary participants brainstormed ideas for applying clinical imaging for the earliest detection of cancers, including the application of artificial intelligence to aid image analysis. Teams were formed and on the final day, each team pitched their project idea to the funding panel. Five projects received £100,000 funding to enable pilot and feasibility studies; one of these involved OxCODE scientists Barbara Braden (Translational Gastroenterology Unit, NDM) and Stephen Taylor (Weatherall Institute of Molecular Medicine).

The Endo.AI project, led by Barbara Braden with team members Steve Taylor, Wei Pang (University of Aberdeen) and Xiaohong Gao (Middlesex University), aims to improve the detection of oesophageal squamous cell carcinoma. These cancers are difficult to detect using standard endoscopy because they are visually only subtly different from the normal oesophagus. To facilitate detection, Endo.AI will both collect more advanced images using dyes or a filtered light spectrum and apply real-time computer algorithms to guide the endoscopist during the examination to focus on regions with suspected cancers for further investigation.  


Cancer Research UK logo        Engineering and Physical Sciences Research Council logo        UKRI Science Technology and Facilities Council logo