New algorithms can help GPs predict which of their patients have undiagnosed cancer

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 anonymised electronic health records from 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. 

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 the 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 onwards, including for some rare types of cancer type." 

ENDS  

NOTES TO EDITORS  

The 15 different types of cancer detected included lung, colorectal, breast, prostate, blood, ovarian, renal, gastro-oesophageal, uterine, pancreas, cervical, oral, testicular, liver, and other. 

Contact: 

Honey Lucas   

Faculty Communications Officer – Medicine and Dentistry   

Queen Mary University of London   

Email: h.lucas@qmul.ac.uk or press@qmul.ac.uk   

Paper details: 

Julia Hippisley-Cox and Carol Coupland. “Development and external validation of prediction algorithms to improve early diagnosis of cancer.” Published in Nature Communications.   
DOI: 10.1038/s41467-025-57990-5 
Available after publication at:  https://www.nature.com/articles/s41467-025-57990-5  

 Under strict embargo until 10am (UK time) on Wednesday 7 May 2025.  

 A copy of the paper is available upon request.  

Conflicts of interest:  

Julia Hippisley-Cox reports grants from National Institute for Health Research, John Fell Oxford University Press Research Fund (award 0008334), Cancer Research U.K. (C5255/A18085), and other research councils, during the conduct of the study. She is an unpaid director of QResearch, a not-for-profit organisation which was a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. Until 9 Aug 2023, she had a 50% shareholding in ClinRisk Ltd. She is a consultant to Endeavour Predict Ltd.  

Carol Coupland reports receiving personal fees from ClinRisk Ltd outside this work. 

Funded by:  This study was funded by the John Fell Fund, University of Oxford. 

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