Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care

Background: Urgent Suspected Cancer (2WW) referrals have been successful in improving early cancer detection but are increasingly a major burden on NHS services. This has been exacerbated by the COVID-19 pandemic. Method: We developed and validated tests to assess the risk of any cancer for 2WW patients. The tests use routine blood measurements (FBC, U&E, LFTs, tumour markers), combining them using machine learning and statistical modelling. Algorithms were developed and validated for nine 2WW pathways using retrospective data from 371,799 referrals to Leeds Teaching Hospitals Trust (development set 2011-16, validation set 2017-19). A minimum set of blood measurements were required for inclusion, and missing data were modelled internally by the algorithms. Findings: We present results for two clinical use-cases. In use-case 1, the algorithms correctly identify 20% of patients who do not have cancer and may not need an urgent 2WW referral. In use-case 2, they identify 90% of cancer cases with a high probability of cancer that could be prioritised for review. Interpretation: Combining a panel of widely available blood markers produces effective blood tests for cancer for NHS 2WW patients. The tests are cost-effective, can be deployed rapidly to any NHS pathology laboratory with no additional hardware requirements, and are of particular value during the COVID-19 pandemic.