Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.700204
Title: Advanced Raman techniques for real time cancer diagnostics
Author: Vardaki, Martha
ISNI:       0000 0004 5992 278X
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2016
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
Cancer is one of the greatest causes of death in modern societies, affecting over 350,000 new cases every year in the UK. Although there are currently more than 100 different cancer types, breast and prostate cancer remain the most common types for women and men respectively. A number of different cancer types follow, with bladder cancer being the ninth most significant type, accounting for 3% of the total new cases. The currently employed techniques aim to diagnose the cancer at an early stage, where the symptoms are easier to be treated and the disease more likely to be cured. A further issue is that many cancers diagnosed will not affect a patient in their lifetime. The current gold standard for cancer diagnosis, biopsy followed by histopathology, is an invasive, restrictive technique and the screening tests suffer from low specificity, the need for a novel diagnostic concept is vital. Furthermore, the current clinical approach does not identify those patients most at risk of advancing disease. A promising approach consists of molecular vibrational spectroscopy techniques, which are based on the interactions of light with matter. One of these is Raman spectroscopy, a technique with wide applications in research and industry, which has the advantage of being non-invasive and chemically highly specific. In this thesis we explore the potential of a group of minimally invasive diagnostic techniques, based on Raman scattering, for prostate, breast and bladder cancer. In the case of the two most prevalent types of cancer, prostate and breast cancer, deep Raman spectroscopy has been employed to study the origin of Raman scattering (Chapters 5 and 6) in animal tissue and tissue phantoms, containing highly scattering materials resembling suspicious features found in tissues (calcifications). The spatial distribution of the Raman signal through the sample volume has been studied in relation to the optical properties and the composition of the sample, showing that a couple of transmission measurements would potentially cover the measuring volume of prostate of typical dimensions. Deep Raman measurements were also extended to animal and human tissue samples, in order to investigate the feasibility of collecting Raman scattering from human prostate tissue and its major tissue components (Chapter 6). Further improvements on these measurements were attempted by introducing the ‘’photon diode’’ element (Chapter 7) in order to achieve signal enhancement, which proved to be in the range of ×1-2.4, depending on the optical properties of the tissue and the depth of the probing element. The same ‘’photon diode’’ concept was utilised to attempt depth prediction of a calcification feature in sample volume (Chapter 8). Regarding bladder cancer, the minimally invasive approach adopted was Raman spectroscopy on urine samples, rather than deep Raman spectroscopy. Raman microscopy was employed in order to discriminate pathological features of bladder cancer between healthy and malignant urine samples. For that reason, the potential differences in urea’s distribution and interactions in urine from healthy and patients with bladder cancer were studied, resulting in promising diagnostic values (73% sensitivity, 80% specificity). The results presented in this thesis are expected to lead to a better understanding of the Raman scattering signals collection through biological tissues and help in this way the future design of Raman instruments aiming to target disease specific signals. This study shows promise for future application of Raman spectroscopy and paves the way towards the future integration of Raman spectroscopy in a non-invasive cancer diagnosis.
Supervisor: Nicholas, Stone Sponsor: STFC Biomedical Network ; EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.700204  DOI: Not available
Keywords: Raman ; spectroscopy ; deep Raman ; phantoms ; photon diode ; prostate cancer ; breast cancer ; bladder cancer ; urine
Share: