Translating Raman spectroscopy for colorectal cancer diagnosis with a focus on high-throughput design, inter-user variability and sample handling considerations.
BackgroundThe majority of colorectal cancers (CRCs) are detected after symptomatic presentation to primary care. Given the shared symptoms of CRC and benign disorders it is challenging to manage this risk of missed diagnosis. Colonoscopy resources cannot keep pace with increasing demand. There is a pressing need for access to simple triage tools in primary care to help prioritise patients for referral.AimTo evaluate the performance of a novel spectroscopy-based CRC blood test in primary care.Design & settingMixed methods pilot study of test performance and GP focus group discussions.MethodUrgent suspected cancer patients were recruited for the Raman spectroscopy (RS) test coupled to machine learning classification (‘Raman-CRC’) to identify CRC within the referred population. Qualitative focus group work evaluated the acceptability of the test in primary care by thematic analysis of focus group theorising.Results532 patients age over 50 referred on the USC pathway were recruited from 27 GP practices. Twenty nine patients (5%) were diagnosed with CRC. Raman-CRC identified CRC with sensitivity 95.7%, specificity 69.3% with Area Under Curve (AUC) of 0.80 as compared to colonoscopy as reference test (248 patients). Stage I/II cancers were detected with 78.6% sensitivity. Focus group themes underlined the convenience of a blood test for the patient and the test’s value as a risk assessment tool in primary care.ConclusionsOur findings support this novel, non-invasive blood-based method to prioritise those patients most likely to have CRC. Raman-CRC may accelerate access to diagnosis with potential to improve cancer outcomes.
Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Ramanspectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information usedfor diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potentialpre-processing combinations, optimisation of pre-processing via a manual ‘trial and error’ format is often time intensivewith no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performancecomputing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection (CRC). The effect of varying pre-processingorder, using extended multiplicative scatter correction (EMSC), spectral smoothing, baseline correction, binning andnormalisation was considered. Permutations were assessed on their ability to detect patients with disease using arandom forest (RF) algorithm with training and testing data sets with 102 patients (510 spectra) and an independent testset of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performancein diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value (PPV)increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processingoptimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnosticsdemonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test anddemonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisationwithout the used of HPC. From the HPC permutations, recommendations for appropriate parameter constraints forconducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchersnot having access to HPC.
Magnetically trapped 87 Rb atoms have been observed near a single-mode optical nanofibre. Approximately 1 × 10 6 atoms were optically pumped to the �F = 2, m F = 2⟩ state and held in the trap with a trap lifetime of up to 2 s. The temperature of the atomic sample within the magnetic trap was measured to be below 230 μK. The compact vacuum system and hightemperature fibre feedthroughs are presented, and the feasibility of creating a quantum degenerate gas of ultracold neutral atoms near an optical nanofibre is discussed.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.