Artificial Intelligence for multi-modal cancer treatment monitoring and tumour recurrence
Sheraz R. Markar, Nuffield Department of Surgical Sciences, University of Oxford
Recent developments in Artificial Intelligence (AI) and Machine Learning (ML), in particular Deep Learning (DL), have been shown to yield results of comparable accuracy to the human experts in various clinical applications. Similarly, recent developments in cancer research, for example to standard oesophageal or gastric cancer treatments, such as surgery, chemotherapy, and radiation therapy, have shown that the overall survival rate for oesophageal cancer has doubled over the last 20 years. Given the data intensive (in particular imaging) surveillance protocols for cancer detection, the project will investigate the use of routinely cancer imaging (e.g. Computed Tomography, Positron Emission Tomography) for development of multimodal, longitudinal AI/ML models that could integrate imaging and non-imaging data to model patient treatment response, survival rates, or predict cancer recurrence. Accurate monitoring of treatment progression and modelling of treatment response are essential steps for development of personalised medicine.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The project encompasses:
- Developing skills in AI/ML techniques with application to Cancer Imaging
- Writing scientific reports and journal articles and presenting findings at the research conferences.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The Big Data Institute (BDI) is a world-renowned research centre and is housed in a brand new state-of-the-art research facility. Full training will be provided in a range of Health Data Science topics (Currently BDI is hosting CDT in Health Data Science for DPhil students). The student will have also opportunity to attend the research seminar offered at the BDI and the Institute of Biomedical Engineering (IBME) as the primary supervisor is a member of Imaging Hub at the IBME (https://eng.ox.ac.uk/biomedical-image-analysis/). The student will be expected to attend relevant seminars within the department and those relevant in the wider University. Subject-specific training will be received through our group's weekly supervision meetings.
The ideal candidate will have:
- Degree in Computer Science, Engineering or related discipline
- Strong programming skills (preferable Python, or Matlab/C++ and willing to learn Python)
- Experience or interest in machine learning (Deep Learning) and Medical Image Analysis (in particular cancer imaging)
- Experience or enthusiasm to work with clinicians/oncologists/surgeons