New Study Hopes to Detect Oesophageal Cancer Early

Researchers at the University of Oxford’s Nuffield Department of Primary Care Health Sciences have unveiled a groundbreaking technology that could revolutionize the early diagnosis of oesophageal cancer – the long tube that transports food from the throat to the stomach. This is the world’s eighth most frequent cancer. The team developed a prediction algorithm called ‘CanPredict (oesophageal)’ using vast patient databases and cutting-edge computational techniques, which identifies individuals at high risk of this cancer and could potentially save countless lives through targeted screening and early intervention.

The team of researchers from the Universities of Oxford, Cambridge, and Nottingham created this innovative tool to predict the 10-year risk of oesophageal cancer and identify high-risk patients for further screening, potentially leading to earlier detection and better patient outcomes. While there are procedures for identifying oesophageal cancer, such as endoscopy, these are frequently reserved for people who have symptoms or are already known to be at high risk.

Professor Julia Hippisley-Cox, a practising GP and lead researcher at the University of Oxford’s Nuffield Department of Primary Care Health Sciences, emphasized the CanPredict tool’s potential impact: ‘With no widespread screening program currently in place in the NHS, developing a new strategy to enable earlier detection remains paramount. CanPredict takes a personalized strategy, focusing on those in greatest need and identifying people at risk of oesophageal cancer. This has the potential to lead to earlier cancer diagnoses when there are more therapy options.’

To put it into perspective, by monitoring only the top 20% of high-risk individuals using CanPredict, we can catch more than 3 in 4 cases (76%) of projected oesophageal cancer diagnoses in the future decade.

Oesophageal cancer, a major public health concern worldwide, frequently goes unnoticed until it is advanced, making early detection critical. This new algorithm has the potential to change the way primary care practitioners, as well as healthcare systems in general, handle the disease. It may be something that a GP practice does a few times a year to identify high-risk patients without requiring them to come in for appointments.

The team created the new technology by analyzing anonymized medical records from over 12 million patients from GP practices throughout England that contributed to the QResearch database, and they discovered over 16,000 cases of oesophageal cancer. The CanPredict algorithm incorporates critical parameters such as age, lifestyle behaviors, medical history, and medication use.

CanPredict was tested in a distinct set of QResearch practices (over 4 million patients) and the Clinical Practice Research Database (over 2.5 million patients) after it was built. CanPredict accurately predicted an individual’s risk of oesophageal cancer over the next decade in testing. It beat existing models for estimating the risk of oesophageal cancer.

Winnie Mei, co-author and Research Fellow in Medical Statistics and Epidemiology at the University of Oxford’s Nuffield Department of Primary Care Health Sciences, said:

‘Our study bridges a significant gap in primary care. By identifying high-risk patients earlier, we can potentially offer them life-saving interventions. This tool is a testament to the power of combining technology with medical research.’

The study also emphasized the significance of age, BMI, smoking, alcohol intake, and past medical disorders in influencing the chance of getting oesophageal cancer. The capacity of the algorithm to incorporate these criteria provides a thorough and personalized risk assessment for individuals, as well as helping the NHS optimize resource allocation by prioritizing those at highest risk who are most likely to benefit from screening.

Professor Rebecca Fitzgerald, OBE, FMedSci, co-author and Professor of Cancer Prevention at the University of Cambridge, said:

‘While our findings are promising, it’s essential to approach them with cautious optimism. Our next steps to realising the potential of CanPredict involve assessing the cost-effectiveness of this tool and exploring its integration into national clinical computer systems.’

Professor Julia Hippisley-Cox said: ‘We thank the many thousands of GPs who share anonymised data with QResearch without whom this research would not be possible.’

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