Our Research Themes
IPOCH will target several major precision medicine challenges to provide training that will deliver the next generation of oncology innovators, researchers, and entrepreneurs. These challenges are:
(a) 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
(b) develop better analytical models to individualise treatment plans based on biomarkers from imaging, radiomics and spatial genomics
(c) implement expert systems for efficiency through technology healthcare (e.g., natural language processing, automated processes, optimised workflows, optimised visualizations, interfaces and interactions with decision support systems).
Our Research Projects
| Project Title | PhD Student | Supervisory Team |
|---|---|---|
| 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 | Integration of Engineered and Deep Learning Radiomics Imaging Features to Characterise Tumour Heterogeneity in Non-Small Cell Lung Cancer |
| Non-invasive Radiomic Classifiers of Radiotherapy Response in Rectal Cancer, | Yiwen Dong | Emiliano Spezi(ENGIN), Richard Adams(MEDIC) |