Statistical analysis and modelling of bi-modal autofluorescence-Raman imaging for efficient diagnosis and treatment of biological tissues

拉曼光谱，一种测量分子振动的特殊类型的光谱技术，已经成功地应用于在足够精细的尺度上确定生物组织的化学组成。这反过来又允许所获取的信息容易地用于皮肤的诊断和手术治疗，以及潜在的其他类型的癌症，例如乳腺癌。该方法有望成为现有诊断和手术实践的更准确、成本更低的替代方案，因此可以更快速、更广泛地获得这些类型的医疗保健。在患者体验方面，该方法还通过显著减少不必要的切除或以其他方式创伤的组织的量以及手术的连续阶段之间的滞后来提供改进。在其简单的实现中，该方法将首先扫描整个生物样品，然后处理来自每个位点的光谱数据，用于随后的自动分析，以确定样品中是否存在癌性形成，或者更雄心勃勃地，在每个探测位置产生生物学描述。然而，这种幼稚的实现需要非常长的时间，从而破坏了在单个不间断手术期间应用该方法的目的。已经提出了一种双模式成像解决方案，其中几乎瞬时的初步自发荧光成像与自动聚类技术相结合，随后引导拉曼光谱仪将其测量集中在被认为更可能包含癌症的片段上。然后，在大量先前分析的样本上训练的统计模型尝试完成每个片段中的癌症检测任务，如果对当前诊断没有足够的信心，则请求更多的拉曼测量。实施这种方法的设备现在已经在一个中心进行了试验，并准备在其他NHS中心以及国际上进行试验。虽然目前报告的结果所产生的当前实施的方法是令人鼓舞的，仍然有几个方向，以推进该方法，并随后将现有的技术转化为尖端的最终产品，将满足医疗保健提供者和患者的期望。特别是，虽然医院的客观评估在很大程度上证实了该方法的预期灵敏度为90%，但特异性（正确诊断为非癌症的非癌症样本的比例）明显低于预期。此外，虽然目前该技术需要长达30分钟的诊断，但最终目标是5-10分钟。我们提出了先进的统计和计算模型和方法，利用以前未充分利用的空间和形态信息，以实现所需的转换。所提出的方法还包括将目前使用的通用多元统计分析升级为功能数据分析，其利用拉曼光谱的固有功能结构，从而从分析的生物组织中提取更准确的生化标记。非欧几里德统计的方法也被认为是有效地捕获和表示光谱数据中的空间变化，并随后使用这样的空间信息更准确地识别组织类型。最近可用数据的扩大也将使我们能够利用更复杂的统计分类模型，捕获组织类型之间的更细微差异，并随后导致更准确和更强大的癌症检测。该提案也得到了我们来自爱沙尼亚（欧盟）的研究合作伙伴的支持，他们将通过调查另一类统计模型来补充我们的工作，从而增加提供高价值最终产品的总体机会。

Raman spectroscopy, a particular type of spectroscopic technique to measure molecular vibrations, has been successfully applied to determine chemical composition of biological tissues at sufficiently fine scales. This has in turn allowed the acquired information to be readily used for diagnosis and surgical treatment of skin, and potentially other types of cancer, such as breast cancer. The approach promises a more accurate and significantly less costly alternative to the existing diagnosis and surgical practices, and therefore a more rapid and broader access to these types of healthcare. In terms of patient experience the approach also offers an improvement through a significant reduction of both, the amount of unnecessarily removed or otherwise traumatised tissue, and the lag between successive stages of the procedure. In its naïve implementation, the method would first scan the entire biological sample before processing the spectral data from each site for subsequent automated analysis to establish presence or absence of cancerous formations in the sample or, more ambitiously, to produce a biological description at each probed location. However, this naïve implementation takes prohibitively long time, defeating the purpose of applying the method during a single uninterrupted surgery. A bi-modal imaging solution has been proposed, in which a nearly instantaneous preliminary autofluorescence imaging combined with an automated clustering technique subsequently guides the Raman spectrometer to concentrate its measurements on segments deemed more likely to contain cancer. A statistical model trained on a large number of previously analysed samples then attempts to complete the cancer detection task in each segment, requesting more Raman measurements if it is not already sufficiently confident in the current diagnosis. A device implementing this methodology has now been trialed in one, and is ready to be trialed in other NHS centres, as well as internationally. While the currently reported results produced by the current implementation of the methodology are encouraging, there still remain several directions for advancing the methodology and subsequently transforming the existing technology to a cutting edge final product that will meet the expectations of the healthcare providers and patients. In particular, while the objective evaluation in the hospital largely confirms the expected sensitivity of the method as 90%, the specificity (proportion of non-cancer samples correctly diagnosed as non-cancer) is notably lower than expected. Also, while currently the technology requires upto 30 minutes to produce a diagnosis, the ultimate aim is 5-10 minutes. We propose advanced statistical and computational models and methods, which utilize previously under-utilized spatial and morphological information to achieve the required transformation. The proposed methods also include upgrading currently used generic Multivariate Statistical Analysis to Functional Data Analysis, which takes advantage of the intrinsic functional structure of the Raman spectra and hence extracts more accurate biochemical markers from the analysed biological tissues. Methods of non-Euclidean statistics are also considered to efficiently capture and represent spatial variation in the spectral data and subsequently use such spatial information for more accurate recognition of the tissue types. The recent enlargement of the available data shall also allow us to take advantage of more complex statistical classification models capturing finer differences between the tissue types and subsequently leading to more accurate and robust detection of cancer. The proposal is also supported by our research partners from Estonia (EU) who are going to complement our work by investigating an additional class of statistical models, increasing the overall chance of delivering a highly valuable final product.