by Angela Guess
A recent press release out of UCLH reports, “A new medical research partnership which aims to develop pioneering technology which can automatically differentiate between cancerous and healthy tissues on patient scans is announced today.The partnership brings together leading clinicians and researchers at University College London Hospitals NHS Foundation Trust (UCLH) with some of the UK’s top technologists at DeepMind Health, which specialises in using machine learning to solve some of the world’s most difficult problems. At present, it can take clinicians up to four hours to identify and differentiate between cancerous and healthy tissues on CT and MRI scans of head and neck cancer patients. This process, known as segmentation, is particularly difficult in head and neck cancer patients because their tumours are situated in extremely close proximity to healthy structures such as the eyes and nerves.”
The release continues, “The purpose of the research collaboration between UCLH and DeepMind is to develop artificial intelligence technology to assist clinicians in the segmentation process so that it can be done more rapidly but just as accurately. Clinicians will remain responsible for deciding radiotherapy treatment plans but it is hoped that the segmentation process could be reduced from up to four hours to around an hour. The research involves anonymised radiotherapy images of up to 700 former head and neck cancer patients who have consented to their data being used for research purposes. Dr Yen-Ching Chang, clinical lead for radiotherapy at UCLH, said: ‘This is very exciting research which could revolutionise the way in which we plan radiotherapy treatment. Developing machine learning which can automatically differentiate between cancerous and healthy tissue on radiotherapy scans will assist clinicians in planning radiotherapy treatment. This has the potential to free up clinicians to spend even more time on patient care, education and research, all of which would be to the benefit of our patients and the populations we serve’.”
Photo credit: UCLH