About the Project
Preoperative chemotherapy is used to treat patients with high-risk breast cancers – such as locally advanced breast cancer or breast cancers with clinically aggressive tumours – to reduce the size of the primary tumour and facilitate surgery and/or radiation treatment. However, this preoperative chemotherapy fails to achieve an optimal response for a significant proportion of women and men diagnosed with this kind of cancer. A suboptimal response is strongly linked to higher risk of cancer recurrence and cancer-related death – however, clinicians are currently unable to predict which patients will respond well to preoperative chemotherapy prior to the treatment starting.
This project proposes the use of an AI-assisted digital pathology platform to analyse tumour biopsies taken from patients diagnosed with breast cancer, so that we can quantify key clinical biomarkers that have been shown to affect a patients’ response to preoperative chemotherapy. This will help us to predict preoperative chemotherapy response before treatment starts, and therefore develop a personalised, tailored treatment regimen to improve patient outcomes.
Key objectives in this programme of research are:
1. The development of an AI-assisted digital pathology platform for high-throughput automated whole slide image analysis using state-of-the-art computer vision techniques.
2. The identification and quantification of relevant clinical biomarkers from the pre-treatment tumour core biopsy using additional AI-based computer vision algorithms.
3. The development of a machine learning based predictive model of preoperative chemotherapy response that can be applied before treatment starts.
The Materials and Engineering Research Institute (MERI) is a dynamic interdisciplinary research institute dedicated to addressing industrial problems through the application of fundamental science and engineering. For information about MERI please visit https://www.shu.ac.uk/research/specialisms/materials-and-engineering-research-institute
Application deadline: applicants accepted all year round with enrolments during September, February (January on website) and May
Duration: 4 years full time, 7 years part time.
Funding Notes
Funding Status: there is no funding attached to this project. The applicant will need to fund their own tuition fees, research costs and living expenses.
For information about how to apply, entry requirements, tuition fees and other costs please visit https://www.shu.ac.uk/research/specialisms/materials-and-engineering-research-institute/research-degrees
