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

使用深紫外拉曼光谱和机器学习分析 (BODICA II) 分析组织和粪便样本进行肠癌筛查

• 批准号: 10067160
• 金额: $ 60.66万
• 依托单位: IS-INSTRUMENTS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10067160
• 关键词: Bowel; Cancer; screening; using; tissue

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

结直肠癌是西方第三常见的癌症，英国每年诊断出超过40，000例病例。在英国，肠癌每年导致16,600人死亡。早期诊断对于提高生存率至关重要，1期肠癌的生存率为90%以上，而4期为10%。国家肠癌筛查计划（NBCSP）在早期阶段检测癌症并改善生存结果，然而，筛查对结肠镜检查服务造成了严重压力。2018年，4，000名患者等待结肠镜检查的时间超过了6周的目标，30%的NHS信托基金违反了同一目标。目前，结直肠癌诊断依赖于结肠镜检查，组织活检和组织病理学分析，这需要时间，延迟最终治疗，并且价格昂贵。在粪便样本中检测癌症生物标志物并在结肠镜检查时准确诊断结直肠癌的能力将使当前的护理途径发生重大变化。这种能力不仅可以加快诊断速度，帮助减少目前的等待名单，它也将节省资金和生命。该项目旨在开发新的仪器，利用拉曼光谱学的进步，以实现这一目标。所提出的方法允许对粪便和组织样本进行研究，从而有可能减少对最初侵入性结肠镜检查的要求。这种双阶段筛查程序提供了检测粪便样本中癌性生物标志物存在的机会，然后可以用于指导（如果需要）在护理点使用相同的拟议仪器对结肠镜来源的活检标本进行进一步检测。利兹大学已经证明，通过拉曼光谱观察，可以从腺瘤和正常结肠组织中识别结直肠癌。通过使用最新的机器学习算法分析拉曼光谱，证明可以区分这些不同的组织类型。我们提出的设备是一种新的深紫外拉曼光谱仪（DUVRS），将作为临床医生友好的仪器在护理点使用，最初的目的是识别粪便样本中潜在的癌症生物标志物。然后，第一阶段筛查将告知临床医生需要进一步调查。然后，该仪器可以在几分钟内用于在内窥镜检查室内识别癌组织、癌前腺瘤和增生性良性息肉。所提出的方法将通过减少和简化诊断要求来挽救生命，同时也大大降低了成本。