Whole-body diffusion weighted magnetic resonance imaging (MRI) has provided novel insight into myeloma bone disease which includes early detection, greater understanding of heterogeneity and non-invasive assessment of response to treatment, anatomy and pace of disease. Using advanced complimentary technologies such as machine learning and radiomics we hope to be able to develop a pipeline to transform these large datasets into meaningful metrics of phenotype, burden and response.
In collaboration with the Myeloma Group we are investigating how whole body MRI biomarkers can be integrated into models of risk to inform clinical decision-making and precision medicine. We have initiated collaborations with several European partners to work towards standardised protocols and analysis tools to facilitate multicentre collaborations and data sharing initiatives.
Our studies of multiparametric MRI of soft tissue sarcoma have provided an invaluable platform for understanding the relationship between functional MRI parameters and histology and have facilitated development of tumour segmentation tools which are based on biologically relevant parameters.
We are developing visual and quantitative maps from MRI data to identify aggressive regions and assess treatment response independent of changes in lesion size (collaboration with Jessica Winfield and Mathew Blackledge, MRI physics) (Figure 1, below). This will facilitate localised treatment planning and response assessment in heterogeneous tumours. We are part of an international collaboration of sarcoma units across Europe and North America and have led and collaborated on several consensus documents.