About the Project
New techniques for the diagnosis of cancer are being developed on the world-leading ALICE accelerator at Daresbury using a state-of-the-art scanning near field optical microscope (SNOM) installed on an infrared free electron laser (IR FEL) by the Liverpool group. In this PhD project we will explore the potential of these techniques to yield a diagnostic for several types of cancer in collaboration with clinical colleagues from the Liverpool Women’s hospital. The project will also explore whether the analysis of IR FEL-SNOM images can yield common features in tissue. Novel statistical algorithms will be used to identify the set of infrared wavelengths that will be subsequently used to classify a set of benign/pre-cancer and cancer tissue.
Early detection of disease and accurate prediction of prognosis are essential to improve patient outcomes for the greatest chance of cure, as well as to optimise healthcare resources. These goals can be achieved by identifying key biomarkers that are a characteristic of a particular disease, and which can be observed from an early stage in the development of the disease. Knowledge of specific biomarkers can improve diagnostic accuracy and predict the most likely response to a particular treatment. Problems in identifying such biomarkers include non-trivial extraction from large amounts of multi-parameter data, signal extraction from noise, and determining specific correlations across parameters in data - which can itself be classed as a biomarker. For example, we have found that discrete wavelength pairing within infrared (IR) absorption spectroscopy data from cancerous tissues shows promise as a sensitive biomarker of disease.
This PhD project, as part of the Imaging Theme of to the EPSRC Liverpool Centre of Maths in Healthcare, will investigate the use of variable selection algorithms to identify the set of infrared wavelengths that will be subsequently used to classify a set of benign/pre-cancer and cancer tissue. Fourier transform infrared spectroscopy (FTIR) and scanning near-field optical spectroscopy (IR-SNOM) data will be obtained and analysed. The correlation matrix generated from SNOM images for the selected range of wavelengths will be used for classification.
The long-term aim is to establish protocols to enhance the diagnosis of potentially malignant abnormal lesions identified in long term monitoring programmes and to develop instruments for in-theatre diagnostics with the potential to identify tumour cells during surgery.
Award conditions: GTA’s will spend most of their time working towards the objectives of the above PhD programme. The teaching assistant duties will take up to 72 hours per semester (a mix of tutorial and marking) during each semester.
Funding Notes
This is a Graduate Teaching Assistant studentship funded for 3.5 years, is fully funded, covers University fees at the level set for UK/EU students plus provides a stipend in line with that paid to students funded by the research councils (tax-free maintenance grant of last year was at £14,296, subject to increase in 2017/18).
The studentship is available to all applicants but University fees are covered only at the UK/EU rate. Overseas candidates would have to provide the difference between the UK/EU and the overseas student rates, for the university fees, from some other source, e.g. scholarship or personal funds.
References
Halliwell, D. E., Morais, C. L. M., Lima, K. M. G., Trevisan, J., Siggel-King, M. R. F., Craig, T., . . . Martin, F. L. (2016). Imaging cervical cytology with scanning near-field optical microscopy (SNOM) coupled with an IR-FEL. SCIENTIFIC REPORTS, 6. doi:10.1038/srep29494
Rowe, F. J., Cheyne, C. P., García-Fiñana, M., Noonan, C. P., Howard, C., Smith, J., & Adeoye, J. (2015). Detection of Visual Field Loss in Pituitary Disease: Peripheral Kinetic Versus Central Static.. Neuro-ophthalmology (Aeolus Press), 39(3), 116-124.
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