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

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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.