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Real-World Validation of PinPoint Blood Tests in the NHS: Multivariable Machine Learning to Predict Cancer Risk in
Matt Neal1, Mark Dean2, Sean Duffy1
1PinPoint Data Science Ltd, Nexus, Discovery Way, West Yorkshire, United Kingdom.
Objective:
To validate the United Kingdom Conformity Assessed-marked PinPoint blood tests, which use machine learning models and routinely available blood analytes to estimate cancer risk in adults referred on urgent suspected cancer pathways in National Health Service (NHS) England.
Patients And Methods:
This work comprises a large-scale, prospective, observational, real-world NHS service evaluation of 9 blood tests, carried out from December 21, 2020 to July 31, 2025. Total of 16,481 patients with urgent suspected cancer referrals were enrolled across 5 secondary care Trusts and 170 General Practitioner surgeries. Real-world performance of the tests was evaluated using a range of diagnostic accuracy statistics.
Results:
Five tests have performance indicating potential clinical utility. Receiver operating characteristic area-under-curve scores (95% CI) for these were: Upper gastrointestinal=0.86 (0.81-0.90), Gynecological=0.81 (0.77-0.85), Lung=0.79 (0.74-0.84), Head & Neck=0.73 (0.68-0.78), and Lower gastrointestinal=0.72 (0.67-0.78), Prioritization of the 10% of highest-risk patients would reduce the number needed to investigate to detect one cancer by a factor of 2.6-6.1.
Conclusion:
This work shows the potential of these tests to improve urgent suspected cancer referral pathways. High-risk patients could be diagnosed more rapidly, leading to potential earlier-stage diagnosis and a better diagnostic experience. Low-risk patients could avoid unnecessary invasive medical testing for cancer. The software can be deployed rapidly across the NHS, without the need for additional hardware.
