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Four new potential MR imaging contrast agents were synthesised. Gadolinium-hematoporphyrin (Gd-H) was produced by inserting gadolinium into the naturally occurring porphyrin,hematoporphyrin.Gadolinium-tetra-carboranylmethoxyphenyl-porphyrin acetate (Gd-TCP)was similarly synthesised by gadolinium insertion into the synthetic porphyrin, 1, 6, 11, 16-tetra-[3-(carboranylmethoxy)phenyl] porphyrin. The monoclonal antibodies, 9.2.27 against melanoma and WM53 against leukaemia cell lines, were conjugated with cyclic anhydride gadolinium-diethylenetriaminepenta-acetic acid (Gd-cDTPAa), yielding the attachment of chelate DTPA to the antibodies. Gadolinium ion was inserted into the chelate DTPA, thus labelled both these antibodies with Gd-DTPA. Overall, with the satisfactory low levels of gadolinium in the liver, kidneys, and spleen, and good tumour uptake, gadolinium antibody conjugates has considerable potential for further diagnostic applications of MR imaging. / Doctor of Philosophy (PhD)

Ye / Genetic and epigenetic alterations significantly contribute to development of human cancer. Genotyping tumour tissue in search for these actionable genetic and epigenetic changes has become routine practice in oncology. However, sampling tumour tissue has significant inherent limitations. It provides only a single snapshot in time, prone to selection bias due to intra-tumour heterogeneity, and cannot always be performed owing to its invasive nature. Circulating tumour DNA (ctDNA) based liquid biopsy provides an effective alternative to invasive tissue sampling and have emerged as a minimally invasive, real-time biomarker. Recent advancements in DNA sequencing technologies have revealed enormous potential of ctDNA to improve tumour detection and stratification. In this review, we critically appraise the role of ctDNA as a liquid biopsy for cancer and evaluate the role of circulating tumour DNA as a diagnostic, prognostic and predictive biomarker. We also highlight some technical challenges and constraints associated with circulating DNA analysis.

Yes / An ultra-wideband microstrip antenna design is considered with respect to applications in breast cancer detection. The underlying design concept is based on ground penetrating radar (GPR). Simulated and measured prototype performance show excellent performance in the input impedance and radiation pattern over the target range from 4 GHz to 8 GHz. The 4 GHz to 8GHz frequency band for microwave imaging perform better in comparison with other microwave frequencies. The antenna also shows a reasonable uniform radiation performance in the broadside direction which contributes to the reduction of clutter levels, thus aiding the reconstruction quality of the final image.

Evaluation using mouse subcutaneous tumour models is a key process in cancer drug development. Tumour material is implanted subcutaneously and tumour growth measured using callipers. However this methodology can have poor reproducibility and accuracy due to observer variation. Furthermore the physical pressure of using callipers can distress the mouse and lead to tumour damage. Non-invasive digital tumour imaging would reduce handling stresses and allow volume determination without physical contact. This thesis focusses on capturing 2D digital images of subcutaneous tumours, then using image processing and machine learning methods to determine 3D volume. The biggest challenge faced was lack of differentiation between tumour and surrounding skin, rendering tumour boundary identification difficult. Whilst image processing methods such as colour segmentation and edge detection were unsuccessful, machine learning proved more successful. Three convolutional neural networks, VGG-Face, VGG-19 and VGG-16 models were evaluated, with VGG-Face producing the best results. Using the layer FC7 before RELU activation for extraction in the VGC-Face model, a tumour recognition rate of 98.86% was achieved. This was increased to 100% through a semi-automatic step with detection repeated on cropped versions of negatively classified images. Finally, volume was determined through extracting image features using the VGG-Face model and conducting partial least squares regression (error of 0.1). This work has successfully demonstrated that with computational methods the volume of subcutaneous tumours can be evaluated through non-invasive digital imaging without need to have contact with the tumour itself, thus offering refinement benefits to the mice as well as eliminating observer bias.