GE Healthcare, the University of Cambridge and Cambridge University Hospitals have agreed to collaborate on developing an application aiming to improve cancer care, with Cambridge providing clinical expertise and data to support GE Healthcare’s development and evaluation of an AI-enhanced application that integrates cancer patient data from multiple sources into a single interface.
Building on research supported by The Mark Foundation for Cancer Research and Cancer Research UK, the collaboration aims to address the problems of fragmented or siloed data and disconnected patient information, which is challenging for clinicians to manage effectively and can prevent cancer patients receiving optimal treatment.
“Thanks to ever-improving technologies, we now generate increasing amounts of complex data for each patient with cancer. These include multiple imaging scans, digital pathology, genomic data, advanced blood tests and treatment information.
"Bringing all this data together to make precise and informed decisions for patients can be hard. We often do this inefficiently and miss important connections between the data,” said Professor Richard Gilbertson, Director of the Cancer Research UK Cambridge Centre, and Head of the Department of Oncology at the University of Cambridge.
This new application would be designed using advanced software engineering and machine learning methods to integrate a variety of patient data including clinical, imaging and genomic data - from diagnosis through every stage of treatment - into one single location.
The aim is to offer all medical teams involved in a patient’s cancer care - medical oncologists, clinical oncologists, surgeons, radiologists, pathologists, clinical nurse specialists and more - simultaneous access to the necessary data and information to allow the medical team to plan the best, most personalised treatment for each of their patients.
The application is expected to be evaluated for ovarian cancer initially in Cambridge and the goal is to evaluate it across the UK, and beyond.
Ovarian cancer is often difficult to treat as most patients present with advanced disease. Although initially 70-80% of patients will respond well to chemotherapy, ultimately most develop chemotherapy resistance leading to treatment failure.
The application may help clinicians have better visibility on how the patient respond to treatment, thus helping them more effectively identify when treatment may require adjustment. If the application is successfully developed, our vision is for it to be expanded for use in breast and kidney cancer patients.
“Healthcare professionals can struggle to easily find and interpret the many different types of patient data information they need to make the best clinical decisions,” said Dr Ben Newton, GM Oncology at GE Healthcare. “Bringing these multiple data streams into a single interface could enable clinicians to make fast, informed and highly personalised treatment decisions throughout a patient’s cancer care pathway.”
Two Addenbrooke’s cancer clinicians aiming to evaluate the application to help patients are consultant oncologist Prof James Brenton, professor of Ovarian Cancer Medicine and a senior group leader at the Cancer Research UK Cambridge Institute; and consultant radiologist Prof Evis Sala, professor of Oncological Imaging in the Department of Radiology, University of Cambridge.
“Aggregating and analysing the substantial amounts of data available would help address an unmet need. Ovarian cancer is an important and complex disease with poor outcomes, and we believe this application would help us deal with its complexity.
“If we can aggregate and integrate relevant data along the care pathway, and visualise the output, it may ultimately lead to clinicians making better-informed decisions and better care.” adds Prof Sala who also co-leads the MFICM at the University of Cambridge.
The development work will be underpinned by GE Healthcare’s Edison platform to integrate data from diverse sources, such as electronic health records (EHR) and radiology information systems (RIS), imaging and other medical device data.
Read an interview with Profs James Brenton and Evis Sala discussing the potential impact of this new research collaboration.