A combined spectral-spatial framework for the diagnosis and stratification of colorectal cancer through advanced fast Raman imaging

Background:
There is a growing collaboration between the Departments of Cell & Developmental Biology and Pathlology at UCL and Renishaw PLC to develop new diagnostic approaches to disease and in the first instance Colorectal cancer (CRC). This disease is a major burden on the UK health system. In 2008 the UK spent over £20M on diagnosis of positive cases of CRC. This underpinned >£200M spent on primary surgical, chemo and radiotherapies. Subsequent management of recurrence added a further £400M to the costs of these diseases. Additionally, the NHS spent over £250M in diagnosing patients found to be free of CRC in the first pass. It has been established that the UK survival rate is poor compared to other EU countries and crucially that this correlates directly with stage of the disease at diagnosis and access to treatment. Histopathology: the current gold standard for CRC diagnosis, is still mainly a subjective technique. This leads to significant issues when identifying patients in early stage disease who could be treated more conservatively and very much more cost effectively. But unfortunately agreement between pathologists for early neoplasias can be very poor. Consequently, more accurate objective methods of identifying those patients who will progress rapidly to advanced disease is vital. Raman analysis provides disease specific molecular signals from unlabelled tissues in a reproducible manner. This opens up the possibility of clinical decision making at early neoplastic stages of disease, where currently the gold standard is shown to have only 50% inter-observer agreement (in classification of dysplasias, where conservative treatment is likely to be most effective).

The timeliness and importance of this project
A PhD student funded through the UCL Impact scheme has established the power or Raman imaging to distinguish early pathological features in Raman chemical imaging maps of biopsy tissue from the human colon.
However the bottleneck in the project is now at the level of data analysis and the building of compelling statistical models capable of supporting automated diagnosis from the Raman microscope image. This is where focusing the skills of a junior research mathematician, statistician or computational scientist with a strong interdisciplinary outlook can have a profound impact. The diagnostic models must take account of the Raman spectra obtained pixel-by-pixel across the tissue sample as well as the geometric features characteristic of the morphological changes of colon cancer. Each of these would be a challenge in their own right but our ambition is to combine the analysis of the very high dimensional information from the spectra with the geometric distribution of characteristic chemical signatures in a combined spectral-spatial analysis. The aim is to deliver a hardware and software combination to monitor CRC disease progression that can ultimately meet the standards of regulators like MHRA and the US FDA.

The technical aspects of the project:
The high-dimensional, and highly structured information, present in Raman microscopic maps carry some fascinating data analysis challenges. It can be regarded as both a set of random fields and as a large collection of multivariate processes. Correlations, which exist at different scales across both the spatial and spectral domain, in addition to the non-stationarity/heterogeneity, present many unique and interesting methodological opportunities. In addition to low-dimensional representations of the spectral components (via PCA or otherwise), this project will also explore the utility of so-termed "bags-of-features" approaches to elicit a distribution of spatial interactions present in the data. It is anticipated that this dual spatial-spectral framework should hold significant advantages over traditional approaches to automated pathology detection.