Three innovative projects have each won up to £35,000 from the Cancer Innovation Challenge with the aim of improving diagnosis, treatment and overall cancer care.

The three projects will research into existing NHS Scotland data and use the insights gained to improve cancer patient care and outcomes in Scotland.

All three successful projects have the potential to have a significant impact on cancer treatment in Scotland in terms of improving diagnosis, treatment and overall care. They now have the funding and the opportunity to develop and demonstrate the feasibility of their innovation over the next three months. Two will then be selected to continue to the next stage of the Cancer Innovation Challenge process which will see them receive further funding of up to £125,000 to develop prototypes over a six month period.

The three projects selected are:

Canon Medical Research Europe. This company is working with NHS Greater Glasgow and Clyde on a project aimed at building a robust assessment tool for malignant pleural mesothelioma (MPM), an asbestos-related cancer with particularly high incidence in Scotland. The lack of such a tool to date has limited the ability to evaluate new therapies for this cancer. Canon seeks to address this using machine learning to automate RECIST scoring, the widely used scoring system for assessing response to cancer treatment, from CT scan information for mesothelioma.

Jayex Technology. Jayex Technology is working with NHS Lothian on a proof of concept focusing on haematology cancers as there is a shortfall of this data in the National Registry. They will seek to standardise and migrate existing data collected by clinicians over 30+ years from legacy systems, to a new, cutting-edge platform, mapped to a global data standard. Advanced analytics tools will enable meaningful data discovery to support clinical decision making. The platform will also enable adoption of precision medicine approaches it allows future mapping of genomics and analysis of unstructured data.

Sharpe Analytics. This Edinburgh-based company will harness the power of machine learning to generate tools for the prediction of outcomes for Scottish cancer patients. It will begin with prognosis modelling for patients with renal cell carcinoma using routinely collected data recorded in repositories such as the Scottish Cancer Registry. This will set the foundation for further work to increase the accuracy of its models by incorporating additional variables, such as genetic markers influencing the likelihood of tumour development. It is also working with NHS Lothian on the project.

All three projects aim to deliver against at least one of these objectives:

  • Enable analysis of unstructured data (e.g. clinical notes, medical imaging)
  • Enable data driven clinical decisions
  • Enable data driven service improvement in the NHS
  • Enable data driven recruitment for clinical trials
  • Enable the adoption of precision medical approaches.

Dr Hilary Dobson OBE, Deputy Director of the Innovative Healthcare Delivery Programme (IHDP) and Clinical Lead on this Cancer Innovation Challenge funding call, said: “The response to this funding call was very strong. The selection criteria spanned clinical, technological, academic and business considerations, crucially with improving patient outcomes at their core. The three successful projects demonstrated really strong possibilities for revolutionising cancer care in this country. We are excited to see how each of them develops during this stage of the process.”

Minister for Business, Innovation and Energy, Paul Wheelhouse said: “We are committed to developing Scotland as a centre for innovation, life sciences and world-class clinical research.

“The £1 million Cancer Innovation Challenge Fund plays a key role in supporting entrepreneurship and new approaches in this crucial area of medicine. This funding will allow these companies to take the next step towards developing new approaches to the diagnosis and treatment of blood, kidney and tissue cancers, using advances in machine learning and automation to deliver better outcomes for patients.”