This award aims to reduce the number of animals needed in longitudinal CT imaging studies by developing a standardised radiomics workflow for preclinical imaging analysis.
CT imaging is a non-invasive method used to monitor disease progression and response to drugs in animal studies and in the clinic. For the former, the technique enables longitudinal studies to be performed reducing the number of animals needed in a study overall. However, in cancer studies the genetic properties of the tumour also need to be validated, for example if the tumour is homogenous or heterogeneous, which requires invasive biopsies or killing animals at various timepoints for tissue samples, increasing the number of animals required. Radiomics is an advancing field in medical imaging to enhance data from existing imaging techniques. Using computer modelling, disease characteristics not able to be discerned by eye can be determined, for example by analysing spatial distribution of signal intensities, which enables clinicians to make more informed decisions. For animal research, radiomics has the potential to avoid the need to use animals for tissue phenotyping.
In this Fellowship, Dr Kathryn Brown will develop computational models to extract radiomics data from small animal CT images of lung cancer. She will use the models to establish relationships between tissue characteristics, phenotype and imaging features to maximise the amount of data that can be gleaned from imaging studies. Kathryn will investigate predictive biological features and radiotherapy responses of tumours in multiple mouse strains, reducing the number of animals needed for longitudinal studies. She will develop skills in phantom imaging, bioluminescence imaging and statistical methods.