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.
Aims
The COVID-19 pandemic necessitated introduction of revised diagnostic pathways for assessing Urgent Suspected Cancer (USC) referrals. Combinations of FIT and MPCT were used to manage referrals and prioritise access to clinical services or invasive tests. The effectiveness of these pathways are evaluated in this study.
Methods
All consecutive patients referred from primary care on the USC pathway between 15th March – 15th June 2020 were included to reflect the effect of full lockdown measures. Data collected included demographics, presenting symptom(s), investigations and timelines and patient outcomes up to 90 days following initial referral.
Results
816 patients across 8 sites in Wales were included in this initial analysis. 52.7% of patients were female with median age 69 (21 – 97) years. Of the 50.7% who had first-line clinical review, 70.5% were virtual consultations. 49.3% had primary investigations, with FIT in 31% of patients and MPCT in 18.3%. This was compliant with locally agreed pathways for 77.3% of referrals. COVID-response pathways achieved a 28.5% reduction in use of colonoscopy as first-line investigation and 84.3% of patients avoided face-to-face consultations altogether during this first wave of the pandemic.
Overall, 5.6% of USC referrals were diagnosed with CRC. Median timescale from diagnosis to treatment for CRC was 82 (4 – 175) days. The NPV for FIT in this cohort was 99.5%. MPCT as the first modality had a NPV of 99%.
Conclusion
A modified investigation pathway maintained cancer diagnosis during the pandemic with improved resource utilisation to that used previously.
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