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Mathematical and statistical evaluation of Positron Emission Tomography (PET) data for the management of cancer


Project Description

Combining advanced imaging technologies with patient-level characteristics, it is now feasible to diagnose, monitor and evaluate the prognosis of cancer patients and adopt appropriate treatment strategies tailored to individual patient leading to the concept of personalised medicine. Positron Emission Tomography (PET) is a sensitive non-invasive procedure that provides in-vivo three-dimensional images of lesions and allows assessment of the changes in physiological and functional pathways in cells or tissues using a diverse range of targeted radiotracers. Hence PET is considered as one of the most efficient and promising imaging techniques for oncologic diagnosis and treatment guidance. The PET data in principle consist of images, mainly in DICOM format, that are processed in order to extract “imaging biomarkers” which are subsequently used for advanced statistical modelling for predictive analysis. The PET framework typically involves stages of image acquisition, image segmentation, feature extraction and data analysis. There are several methodological and technical challenges in each of these stages as these involve an integrated application of diverse mathematical algorithms, statistical methodologies and computational tools. In clinical settings, in addition, we also have access to other valuable patient-level data that include a range of physical, physiological and biochemical variables. Therefore, there is immense potential to integrate clinical and imaging data to develop a predictive analytical tool towards meeting the goal of personalised medicine. The project is aimed towards identifying and evaluating the current state of the art methodologies available to analyse the PET data particularly in the context of cancer tissues, exploring and implementing novel methodological options relevant to such data, evaluating predictive modelling options integrating clinical and imaging data and proposing an optimised protocol to enhance the reproducibility of outcomes. The project will, therefore, target several key gaps in the current methodologies with implications in wider subject areas of medical imaging research to improve the current practice and enhanced reproducibility. The project will identify, evaluate and propose novel methodological options, explore the complex relationship between the multiple sources of data, identify feature-based representations of these associations and evaluate the predictive performance of the integrated statistical model in real datasets. The project will provide an excellent opportunity for a prospective student to work in one of the most active and exciting areas of medical research: the student will develop key knowledge and skills of recent developments in imaging research, learn novel mathematical and statistical modelling approaches particularly methodologies involving high-dimensional data analysis and apply these advanced tools and techniques to real clinical datasets.

A total of £5000 per annum will be available for attending the conferences / meetings for the student and purchasing licensed software.

APPLICATION PROCEDURE:
This project is advertised in relation to the research areas of APPLIED HEALTH SCIENCE. Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php. You should apply for Degree of Doctor of Philosophy in Applied Health Science, to ensure that your application is passed to the correct person for processing.

NOTE CLEARLY THE NAME OF THE SUPERVISOR AND EXACT PROJECT TITLE ON THE APPLICATION FORM. Applicants are limited to applying for a maximum of 3 applications for funded projects. Any further applications received will be automatically withdrawn.

Funding Notes

This project is funded by a University of Aberdeen Elphinstone Scholarship. An Elphinstone Scholarship covers the cost of tuition fees only, whether home, EU or overseas.

For details of fees: View Website

Candidates should have (or expect to achieve) a minimum of a First Class Honours degree in a relevant subject. Applicants with a minimum of a 2:1 Honours degree may be considered provided they have a Distinction at Masters level.

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