Bowel Cancer screening using tissue and faecal sample analysis using the Deep-UV-Raman Spectroscopy and Machine Learning Analysis (BODICA II)

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Colorectal cancer is the 3rd most common cancer in the West, with more than 40,000 cases diagnosed in the UK per annum. Bowel cancer is responsible for 16,600 deaths each year in the UK. Early-stage diagnosis is critical to improving survival rates, with stage 1 bowel cancer having a 90%+ survival rate compared to 10% for stage 4\. The National Bowel Cancer Screening Programme (NBCSP) detects cancers at an earlier stage and improves survival outcomes, however, the screening has placed severe pressure on colonoscopy services. In 2018, 4,000 patients were waiting longer than the 6-week target for colonoscopy, with 30% of NHS Trusts in breach of the same target.Currently, colorectal cancer diagnosis relies on colonoscopy with tissue biopsy and histopathology analysis, which takes time, delays definitive treatment, and is expensive. The ability to detect cancer biomarkers in stool samples and accurately diagnose colorectal cancer at the time of colonoscopy would yield a step-change in current care pathways. This ability would not only speed up diagnosis helping to reduce current waiting lists, it would also save money and lives.This project seeks to develop new instrumentation exploiting advances in Raman spectroscopy to achieve this aim. The proposed approach allows both stool and tissue samples to be investigated, thereby offering the potential to reduce the requirement for an initially invasive colonoscopy. This dual-stage screening program provides the opportunity to detect the presence of cancerous biomarkers in stool samples which can then be used to guide, if required, further testing of colonoscopy-sourced biopsy specimens at the point of care using the same proposed instrument.The University-of-Leeds has demonstrated that colorectal cancer can be identified from adenomas and normal colonic tissues through Raman spectroscopic observations. By analyzing the Raman spectra with the latest Machine learning algorithms it was demonstrated that these different tissue type could be differentiated.Our proposed device is a new Deep-UV-Raman-Spectrometer (DUVRS) would be utilized at the point-of-care as a clinician-friendly instrument with the initial aim to identify potential cancer biomarkers in stool samples. This first-stage screening would then inform the clinician of the requirement to investigate further. The instrument could then be used to identify cancerous tissue, precancerous adenomas and hyperplastic benign polyps within the endoscopy suite in a few minutes. The proposed approach will save lives by reducing and simplifying diagnostic requirements, whilst also producing significant cost reductions