A new method to triage colorectal cancer referrals using serum Raman spectroscopy and machine learning

Cerys A Jenkins, Susan Chandler, Rhys Jenkins, Kym Thorne, Freya Woods, Andrew Cunningham, Kayleigh Nelson, Rachel Still, Jenna Walters, Non Gywnne, Wilson Chea, Rachel Harford, Claire O'Neill, Julie Hepburn, Ian Hill, Heather Wilkes, Greg Fegan, Peter Dunstan, Dean A Harris

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Received Date: 3rd May 20

Suspected colorectal cancer (CRC) referrals based on non-specific symptoms currently lead to large numbers of patients being referred for invasive investigations and poor yield in cancer detection. Secondary care diagnostics, particularly endoscopy, struggle to meet the ever-increasing demand and patients face lengthy waits from the point of referral. Here we propose a blood test utilising high-throughput Raman spectroscopy and machine learning as an accurate triage tool. We present results from the first mixed methods clinical validation study of its kind, evaluating the ability of the test to perform in its target population of primary care patients, and its acceptability to those administering and receiving the test. The test was able to accurately rule out cancer with a negative predictive value of 98.0%. This performance could reduce the number of invasive diagnostic procedures in the cohort by at least 47%. Collectively, our findings promote a novel, non-invasive solution to triage CRC referrals with potential to reduce patient anxiety, accelerate access to treatment and improve outcomes.

Read in full at medRxiv.

This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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