Two new advanced predictive algorithms use information about a person's health conditions and simple blood tests to accurately predict a patient's chances of having a currently undiagnosed cancer, including hard-to-diagnose liver and oral cancers. The new models could revolutionise how cancer is detected in primary care, and make it easier for patients to get treatment at much earlier stages.

The NHS currently uses prediction algorithms, such as the QCancer scores, to combine relevant information from patient data and identify individuals deemed at high risk of having a currently undiagnosed cancer, enabling GPs and specialists to call them in for further testing.

Researchers from Queen Mary University of London and the University of Oxford have used the anonymized electronic health records of over 7.4 million adults in England to create two new algorithms which are much more sensitive than existing models, and which could lead to better clinical decision-making and potentially earlier diagnosis of cancer.

The work has been published in Nature Communications.

Crucially, in addition to information about a patient's age, family history, medical diagnoses, symptoms, and general health, the new algorithms incorporated the results of seven routine blood tests (which measure a person's full blood count and test liver function) as biomarkers to improve early cancer diagnosis.

Compared with the existing QCancer algorithms, the new models identified four additional medical conditions associated with an increased risk of 15 different cancers, including those affecting the liver, kidneys, and pancreas. Two additional associations were also found for family history with lung cancer and blood cancer, and seven new symptoms of concern (including itching, bruising, back pain, hoarseness, flatulence, abdominal mass, dark urine) were identified as being associated with multiple cancer types.

These results showed that the new algorithms offer much improved diagnostic capabilities, and in fact are the only ones currently which can be used in primary care settings to estimate the likelihood of having a current but as yet undiagnosed liver cancer.

Professor Julia Hippisley-Cox, Professor of Clinical Epidemiology and Predictive Medicine at Queen Mary University of London, and lead author of the study, said, "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.

"They use existing blood test results which are already in the patients' records, making this an affordable and efficient approach to help the NHS meet its targets to improve its record on diagnosing cancer early by 2028."

Dr. Carol Coupland, senior researcher at Queen Mary University of London and Emeritus Professor of Medical Statistics in Primary Care at the University of Nottingham, and co-author, said, "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.

"They offer the potential for enabling earlier cancer diagnoses in people from the age of 18 onward, including for some rare types of cancer."