The real-time molecular characterisation of human brain tumours during surgery using Rapid Evaporative Ionization Mass Spectrometry [REIMS] and Raman spectroscopy: a platform for precision medicine in neurosurgery

Aim: To investigate new methods for the chemical detection of tumour tissue during neurosurgery. Rationale: Surgeons operating on brain tumours currently lack the ability to directly and immediately assess the presence of tumour tissue to help guide resection. Through developing a first in human application of new technology we hope to demonstrate the proof of concept that chemical detection of tumour tissue is possible. It will be further demonstrated that information can be obtained to potentially aid treatment decisions. This new technology could, therefore, become a platform for more effective surgery and introducing precision medicine to Neurosurgery. Methods: Molecular analysis was performed using Raman spectroscopy and Rapid Evaporative Ionization Mass Spectrometry (REIMS). These systems were first developed for use in brain surgery. A single centre prospective observational study of both modalities was designed involving a total of 75 patients undergoing craniotomy and resection of a range of brain tumours. A neuronavigation system was used to register spectral readings in 3D space. Precise intraoperative readings from different tumour zones were taken and compared to matched core biopsy samples verified by routine histopathology. Results: Multivariate statistics including PCA/LDA analysis was used to analyse the spectra obtained and compare these to the histological data. The systems identified normal versus tumour tissue, tumour grade, tumour type, tumour density and tissue status of key markers of gliomagenesis. Conclusions: The work in this thesis provides proof of concept that useful real time intraoperative spectroscopy is possible. It can integrate well with the current operating room setup to provide key information which could potentially enhance surgical safety and effectiveness in increasing extent of resection. The ability to group tissue samples with respect to genomic data opens up the possibility of using this information during surgery to speed up treatment, escalate/deescalate surgery in specific phenotypic groups to introduce precision medicine to Neurosurgery.Open Acces

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point