Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing.

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (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. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

A high-throughput serum Raman spectroscopy platform and methodology for colorectal cancer diagnostics.

Vibrational spectroscopic techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR) have huge potential for the analysis of biological specimens. The techniques allow the user to gain label-free, non-destructive biochemical information about a given sample. Previous studies using vibrational spectroscopy with the specific application of diagnosing colorectal diseases such as cancer have mainly focused on in vivo or in vitro studies of tissue specimens using microscopy or probe based techniques. There have been few studies of vibrational spectroscopic techniques based on the analysis of blood serum for the advancement of colorectal cancer diagnostics. With growing interest in the field of liquid biopsies, this study presents the development of a high-throughput (HT) serum Raman spectroscopy platform and methodology and compares dry and liquid data acquisition of serum samples. This work considers factors contributing to translatability of the methodologies such as HT design, inter-user variability and sample handling effects on diagnostic capability. The HT Raman methods were tested on a pilot dataset of serum from 30 cancer patients and 30 matched control patients using statistical analysis via cross-validated PLS-DA with a maximum achieved a sensitivity of 83% and specificity of 83% for detecting colorectal cancer.