Researchers from Queen Mary University of London and the University of Oxford have developed innovative algorithms that could significantly enhance early cancer diagnosis in primary care through the use of common health data and routine blood tests.
Two groundbreaking predictive algorithms have emerged, promising a significant leap forward in the early detection of undiagnosed cancers, particularly those difficult to identify through traditional means. Developed through a collaboration between Queen Mary University of London and the University of Oxford, these models leverage anonymized data from over 7.4 million adults in England.
The new algorithms use patient health information and results from seven routine blood tests — which measure full blood count and liver function biomarkers — to more accurately predict the presence of undiagnosed cancers.
This superior sensitivity could transform how general practitioners (GPs) identify and diagnose cancer, leading to earlier and potentially life-saving treatments.
Existing prediction algorithms, like the QCancer scores used by the UK’s National Health Service (NHS), already integrate patient data to flag high-risk individuals.
However, these new models provide a far more sensitive approach, using additional health markers, and enhancing the algorithms’ ability to identify 15 types of cancers, including liver, kidney and pancreatic cancers, as well as rare forms.
“These algorithms are designed to be embedded into clinical systems and used during routine GP consultations. They offer a substantial improvement over current models, with higher accuracy in identifying cancers — especially at early, more treatable stages,” lead author Julia Hippisley-Cox, a professor of clinical epidemiology and predictive medicine at Queen Mary University of London, said in a news release.
The detailed analysis revealed that the new models identified four additional medical conditions and seven new symptoms associated with an increased cancer risk. Symptoms like itching, bruising, back pain, hoarseness, flatulence, abdominal mass and dark urine were flagged as significant for various cancers, enhancing the algorithms’ diagnostic capabilities.
Co-author Carol Coupland, a senior researcher at Queen Mary University of London, emphasized the broad applicability of these algorithms.
“These new algorithms for assessing individuals’ risks of having currently undiagnosed cancer show improved capability of identifying people most at risk of having one of 15 types of cancer based on their symptoms, blood test results, lifestyle factors and other information recorded in their medical records,” she added.
The development of these algorithms comes at a crucial time as health systems globally aim to improve their cancer detection rates. The innovation promises not just enhanced diagnostic precision but also cost-effective integration into existing healthcare frameworks, making it an attractive solution for widespread implementation.
By harnessing readily available patient data and routine blood test results, these algorithms hold the potential to significantly improve early cancer diagnosis, aligning with the NHS’s targets to enhance early cancer detection by 2028.
The research, published in Nature Communications, underscores the importance of leveraging advanced data analytics in health care, offering a beacon of hope for many patients and a promising tool for health care providers worldwide.
Source: Queen Mary University of London