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Related Experiment Video

Updated: Jul 4, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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.

Mayo Clinic Proceedings. Digital Health

|July 3, 2026

Summary

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Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

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UKCA-marked PinPoint blood tests show promise in estimating cancer risk for urgent referrals. Five tests demonstrated clinical utility, potentially improving diagnosis speed and patient experience in the NHS England.

Area of Science:

Oncology
Medical Diagnostics
Machine Learning in Healthcare

Background:

Urgent suspected cancer referral pathways in the National Health Service (NHS) England face challenges in timely and accurate risk stratification.
The integration of machine learning (ML) models utilizing routinely available blood analytes offers a novel approach to cancer risk estimation.

Purpose of the Study:

To validate the performance of United Kingdom Conformity Assessed (UKCA)-marked PinPoint blood tests for estimating cancer risk in adults referred via urgent suspected cancer pathways.
To assess the real-world diagnostic accuracy and potential clinical utility of these ML-based blood tests within the NHS England service.

Main Methods:

A large-scale, prospective, observational, real-world NHS service evaluation involving 16,481 patients with urgent suspected cancer referrals.

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Enrollment across 5 secondary care Trusts and 170 General Practitioner surgeries from December 2020 to July 2025.

Evaluation of real-world test performance using a range of diagnostic accuracy statistics, including receiver operating characteristic area-under-the-curve (ROC AUC).

Main Results:

Five of the nine evaluated blood tests demonstrated potential clinical utility.
Achieved ROC AUC scores included: Upper gastrointestinal=0.86, Gynecological=0.81, Lung=0.79, Head & Neck=0.73, and Lower gastrointestinal=0.72.
Prioritizing the highest-risk 10% of patients could reduce the number needed to investigate by a factor of 2.6-6.1, indicating improved efficiency.

Conclusions:

The PinPoint blood tests show potential to significantly enhance urgent suspected cancer referral pathways in the NHS.
Rapid diagnosis for high-risk patients may lead to earlier-stage detection and an improved patient diagnostic journey.
Low-risk patients could potentially avoid unnecessary invasive procedures, while the software's rapid deployability offers system-wide benefits without new hardware requirements.