Projects
We're committed targeting several major precision medicine challenges to provide training that will deliver the next generation of oncology innovators, researchers, and entrepreneurs.
These challenges are:
- Integrating data to unlock deeper insights: integrating image and genomic data analytics, combined with an appreciation of big data and AI approaches and underpinned by a robust understanding of data integration including data security and data ethics
- Developing truly personalised treatment: develop better analytical models to individualise treatment plans based on biomarkers from imaging, radiomics and spatial genomics
- Building smart systems for greater efficiency: implementing expert systems for efficiency through technology healthcare (e.g., natural language processing, automated processes, optimised workflows, optimised visualisations, interfaces and interactions with decision support systems).
Projects
| Project title | PhD student | Supervisory team |
|---|---|---|
| Multi-omics informed drug development for advanced breast cancer treatment solutions. | Emily Berry | Emiliano Spezi (ENGIN) Richard Adams (MEDIC) |
| Simulation and synthesis of realistic textural phantoms for applications in quantitative medical imaging | Eslam Barzanji | Emiliano Spezi (ENGIN) Geraint Lewis (ENGIN) |
| Artificial Intelligence assisted grading of prostate cancer progression in patient biopsies with novel tissue labelling biomarkers. | Michail Papachristos | Dimitris Parthimos (MEDIC), Rachel Errington (MEDIC), Carolina Fuentes (COMSC), Emiliano Spezi (ENGIN) |
| Microstructural imaging of the tumour microenvironment: towards virtual biopsy of prostate cancer. | Solanki Mitra | Emiliano Spezi (ENGIN), Marco Palombo (COMSC), Kieran Foley (MEDIC) |
| Cancer patients digital twins to investigating disease fragmentation and its impact on drug response in AML trials. | Oisin Brady | Carolina Fuentes (COMSC), Caroline Alvares (MEDIC), Jo Zabkiewz (MEDIC), Peter Giles (MEDIC) |
| Artificial Intelligence with human in the loop for automated medical image contouring. | Faye Warren | Rhodri Smith (MEDIC), Stephen Paisey (MEDIC), Yukun Lai (COMSC), Emiliano Spezi (ENGIN) |
| Non-invasive characterisation of brain cancer tissue microstructure from MRI using deep learning. | Adam Threlfall | Leandro Beltrachini (PHYSX), Marco Palombo (COMSC), Derek Jones (PSYCH), Emiliano Spezi (ENGIN) |
| Integration of engineered and deep learning radiomics imaging features to characterise tumour heterogeneity in non-small cell lung cancer. | Mengcheng Li | Emiliano Spezi (ENGIN), Rhodri Smith (MEDIC) |
| Non-invasive radiomic classifiers of radiotherapy response in rectal cancer. | Yiwen Dong | Emiliano Spezi (ENGIN), Richard Adams (MEDIC) |