Integrating Clinical Infrared and Raman Spectroscopy with digital pathology and AI: CLIRPath-AI

将临床红外和拉曼光谱与数字病理学和人工智能相结合：CLIRPath-AI

• 批准号: EP/W00058X/1
• 负责人: Peter Gardner
• 金额: $ 101.84万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW00058X%2F1
• 关键词: Integrating; Clinical; Infrared; Raman; Spectroscopy

A key feature of the diagnosis of any disease, but particularly various forms of cancer, is the critical information obtained through a biopsy. A biopsy involves the removal of a small sample of tissue, or a few cells, from the patient for examination by a pathologist looking down an optical microscope. In current practice is that the sample is stained with a combination of dyes to help gain some contrast in the image which helps the pathologist see the cells. Generally, based upon this visual inspection of the sample and other relevant medical information, a diagnosis is made. This process, however, is far from ideal since it relies on the expertise of the clinician concerned and is subject to intra in inter observer error. (In other words the process is not exact and depends upon the opinion of the clinicians which may differ). Recently a number of developments have been made in the field of Digital Pathology and Artificial Intelligence (AI). This is where a high resolution photograph of the biopsy slide is taken and examined by a computer algorithm which helps the pathologist make a diagnosis. However analysing the data from just the visible region of the spectrum severely restricts information content of the images obtained. Recently a number of proof of concept studies have shown that molecular spectroscopic techniques such as infrared and Raman are capable of distinguishing diseased from non-diseased cells and tissue based upon the inherent chemistry contained within the cells. (These regions of the spectrum have 40 times the bandwidth of the visible and therefore contain 40 times the amount of information.) The UK is at the forefront in developments associated with both Digital Pathology and AI, the latter augmented by five new technology centres funded by the Industrial Strategy Challenge Fund. In addition, partly due to an EPSRC funded network (CLIRSPEC) the UK is also world leading in the field biomedical infrared and Raman spectroscopy. At present however the Digital pathology/AI and biomedical infrared/Raman these two communities are separate and are not interacting. As a result therefore, the advances made in one area are not being translated to another. In both areas of research there are many hurdles that need to be overcome if this technology is to move from the proof of concept stage through the translational stage and into the clinical setting. It is the belief of the academic community that we are much more likely to overcome these hurdles if we pool our resources, bring in both industrial and clinical partners and work on these generic problems together. This application is for funding to support such a network of partners that will develop dynamic and synergistic interaction between these separate communities for the next four years, for the specific aim of benefiting patients.

诊断任何疾病的一个关键特征，特别是各种形式的癌症，是通过活检获得的关键信息。活组织检查包括从患者身上取出一小块组织样本或几个细胞，供病理学家在光学显微镜下进行检查。在目前的实践中，样本用染料的组合染色，以帮助在图像中获得一些对比度，这有助于病理学家看到细胞。通常，基于样本的这种视觉检查和其他相关的医学信息，进行诊断。然而，该过程远不理想，因为其依赖于相关临床医生的专业知识并且受到观察者间误差的影响。(In换句话说，该过程并不精确，并且取决于可能不同的临床医生的意见）。最近，在数字病理学和人工智能（AI）领域取得了一些进展。这是活检载玻片的高分辨率照片，并通过计算机算法进行检查，以帮助病理学家做出诊断。然而，分析来自光谱的可见光区域的数据严重限制了所获得的图像的信息内容。最近，许多概念验证研究已经表明，分子光谱技术如红外和拉曼能够基于细胞内包含的固有化学将患病细胞和组织与非患病细胞和组织区分开。（这些光谱区域的带宽是可见光的40倍，因此包含的信息量是可见光的40倍。英国在数字病理学和人工智能的发展方面处于领先地位，人工智能由工业战略挑战基金资助的五个新技术中心增强。此外，部分由于EPSRC资助的网络（CLIRSPEC），英国在生物医学红外和拉曼光谱领域也处于世界领先地位。然而，目前数字病理学/人工智能和生物医学红外/拉曼这两个社区是分开的，没有相互作用。因此，在一个领域取得的进展没有转化为另一个领域。在这两个研究领域，如果这项技术要从概念验证阶段进入转化阶段并进入临床环境，则需要克服许多障碍。学术界认为，如果我们集中资源，引入工业和临床合作伙伴，共同解决这些一般性问题，我们更有可能克服这些障碍。该申请旨在资助这样一个合作伙伴网络，该网络将在未来四年内在这些独立的社区之间建立动态和协同的互动，以使患者受益。

项目成果

Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy

• DOI: 10.1002/cam4.7094
• 发表时间: 2024-03-01
• 期刊: CANCER MEDICINE
• 影响因子: 4
• 作者: Whitley，Conor A.;Ellis，Barnaby G.;Risk，Janet M.
• 通讯作者: Risk，Janet M.

Weakly supervised anomaly detection coupled with Fourier transform infrared (FT-IR) spectroscopy for the identification of non-normal tissue.

弱监督异常检测与傅里叶变换红外 (FT-IR) 光谱相结合，用于识别非正常组织。

• DOI: 10.1039/d3an00618b
• 发表时间: 2023
• 期刊: The Analyst
• 作者: Ferguson D