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Contact the study team using the details below to take part. If there are no contact details below please ask your doctor in the first instance.
Dr
Chen-Chun
Pai
chen-chun.pai@medsci.ox.ac.uk
Dr
Alistair
Easton
alistair.easton@oncology.ox.ac.uk
More information about this study, what is involved and how to take part can be found on the study website.
Colorectal cancer
This information is provided directly by researchers, and we recognise that it isn't always easy to understand. We are working with researchers to improve the accessibility of this information. In some summaries, you may come across links to external websites. These websites will have more information to help you better understand the study.
Artificial intelligence (AI), particularly machine learning (ML), is set to revolutionize cancer research and clinical care by speeding up research, improving diagnostics, and enabling personalized treatment plans. This is especially promising for colorectal (CRC) adenocarcinoma, where specific algorithms have been developed. Digitizing pathology processes within the NHS enhances benefits for CRC patients, allowing pathologists to work remotely and collaboratively. The main benefits include improved workflow and the creation of teaching datasets for ML technology, leading to more reproducible and objective analyses with faster turnaround times. ML can alleviate the workload crisis in clinical pathology by flagging cases for further investigation and providing detailed analysis. Digital pathology represents the future, integrating ML into current care pathways to improve diagnostics and prognostics. This study aims to demonstrate that ML algorithms can run alongside routine pathology, providing timely diagnostic and prognostic information without delaying treatment decisions. It also seeks to evaluate the impact of these algorithms on clinical decision-making. Oxford-based research groups have developed ML algorithms to enhance treatment pathways for CRC patients, leveraging the digitization of clinical pathology to improve diagnostics and prognostication. The PACES study will explore how these algorithms can support or change clinical treatment decisions. As AI in the form of ML is a new and untested healthcare technology, this study aims to determine its use by clinicians and integration into existing care pathways with accredited algorithms. PACES is a clinical utility study focused on care pathways and the potential impact on treatment decisions. To avoid bias, clinicians will receive algorithm results only after making treatment decisions.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
You can take part if:
You may not be able to take part if:
1. Any other significant disease or disorder which, in the opinion of the investigator, may either put the participants at risk because of participation in the trial, or may influence the result of the trial, or the participant’s ability to participate in the trial. 2. Treatment with chemoradiotherapy prior to diagnostic biopsy related to the cancer under study in the past 12 months.
Below are the locations for where you can take part in the trial. Please note that not all sites may be open.
Dr
Chen-Chun
Pai
chen-chun.pai@medsci.ox.ac.uk
Dr
Alistair
Easton
alistair.easton@oncology.ox.ac.uk
More information about this study, what is involved and how to take part can be found on the study website.
The study is sponsored by University of Oxford and funded by University of Oxford.
Your feedback is important to us. It will help us improve the quality of the study information on this site. Please answer both questions.
You can print or share the study information with your GP/healthcare provider or contact the research team directly.
