The survival rate of breast cancer, a major and expanding societal challenge, has improved significantly in the past decades, following the introduction of wide range of cancer drugs. Neoadjuvant chemotherapy (NACT), supported by new drugs, is increasingly prescribed to patients with locally advanced breast cancer (LABC) to improve surgical outcome. However, a considerable portion of patients do not respond to NACT, leading to delayed surgery and exposure to drug toxicity. Currently, typically 6 cycles of NACT is prescribed, with treatment evaluated at 3 cycles based on crude tumour size using ultrasound or mammography. Hence, a non-invasive imaging method with early sensitivity to treatment response is essential for further improving patient care.
Dynamic contrast enhanced (DCE) MRI is a powerful tool for measuring tissue perfusion, particularly valuable in breast cancer diagnosis and treatment monitoring. Images are continuously acquired following intravenous injection of a contrast agent, allowing the quantification of permeability through the observation of temporal signal change. Furthermore, it is increasingly recognised that the spatial variation within the tumour is an indicator of treatment resistance. We have acquired a series of DCE MRI data from patients with breast cancer undergoing chemotherapy, and aim to extract critical quantitative permeability and intratumoural heterogeneity to improve patient care.
In this project, you will
• Investigate quantitative methods of T1 calculation and mapping of tissue permeability and leakage volume (Ktrans , and ve) which are important measures of breast tumour malignancy. This will be done using either existing packages (Rocketship, TOPPCAT, R functions) or bespoke tools.
• Calculate semi quantitative maps of measures tissue enhancement characteristics e.g relative enhancement, time to peak etc. Tumors will be segmented from these and texture analysis will be performed using radiomics libraries available on PYTHON and MATLAB.
• Integrate the quantitative methods and texture analysis as a single data processing pathway. Apply this pathway in conjunction with complimentary clinical information to identify early sensitive marker for NACT.
We are seeking a highly-motivated student with a background in a quantitative discipline such as physics, mathematics, engineering or computing science to join our cancer imaging team. Instruction in MRI physics can be provided through an internationally-renowned taught MSc module. The student will join a rapidly-expanding multidisciplinary team to develop and translate novel MRI methods for clinical cancer applications.
Aberdeen has one of the largest single-site medical school campuses in Europe, providing internationally-renowned MSc training in Medical Physics and a highly-rated undergraduate medical degree programme (ranked No 1 in Scotland and No 4 in the UK). Located in the heart of the medical school campus, Aberdeen Biomedical imaging Centre (ABIC) has a long tradition in medical imaging, and has the infrastructure for complex clinical imaging studies. The supervisory team has extensive experience and a strong track record in the development and clinical application of novel MRI methods, including hardware development, image analysis and programming. The successful candidate will be guided by Dr Jiabao He on MRI method development and clinical translation, Dr Trevor Ahearn on DCE physics and analysis, Prof David Lurie on MRI physics, as well as being supported by a group of highly-energetic fellow students within the imaging research programme.
At the end of the PhD, the student will have acquired transferable skills including numerical simulation, image data analysis, clinical study management and MRI scanner programming, essential for the next stage in their career development. The student will benefit from a set of internal training programmes, specially designed to support the success of scientists working in the clinical environment.
For informal discussion, please email the lead investigator of cancer imaging research, Dr Jiabao He ([email protected]
This project is advertised in relation to the research areas of MEDICAL SCIENCES. Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
. You should apply for Degree of Doctor of Philosophy in Medical Sciences, 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.