Glioblastoma multiforme (GBM) is associated with a median survival of just over one year and is the most common and aggressive malignant brain tumour. Brain biopsy is the gold standard for classification of histology and molecular characteristics but complicated by tumour heterogeneity. Routinely acquired NHS brain MR imaging allows diagnosis and tumour segmentation for surgery and radiotherapy planning. Quantitative analysis of tumour volume image characteristics, termed Texture Analysis (TA), can allow prediction of patient survival, glioma grade and molecular status, but is not routinely done.

The aims of this PhD studentship are: a) to develop new multiscale computer code for TA incorporating the best existing methods and b) combine it with machine learning to determine accuracy of prediction of patient survival and tumour characteristics. Multiscale tumour growth models will be further developed and fitted to NHS scan data, to c) determine whether this added longitudinal information enhances the accuracy of individual patient predictions.

This project will suit a student with a strong mathematics and/or computer science background interested in applying their expertise to medical research. Training will be provided in the clinical context of this translational study, neuroimaging, machine learning and computational modelling. The project is offered through an exciting new doctoral training programme that will expand interdisciplinary cancer research at the University of Dundee, focussing on translational research in precision cancer medicine. The DTP is open to scientifically qualified applicants and also clinicians who wish to undertake research training.

For details of this project with supervisors Prof Douglas Steele, Dr Kismet Ibrahim and Dr Dumitru Trucu, go to

The deadline for application is 22 March 2021