My project focuses on the investigation of the value of imaging biomarkers to predict outcomes on cancer survivors (in the post- chemotherapy / post – radiotherapy context).
Quantitative MRI techniques enable the assessment of bone marrow composition, and these tools have expanded our understanding of marrow adiposity. Evidence suggests that cancer therapy alters bone marrow fat and cellular composition, and like fat deposits in other locations, that have an endocrine function, that influences systemic metabolism and potentially contributes to treatment-related morbidity.
The significance of this in cancer survival remains unclear. In particular, the relationships between post-treatment changes in marrow composition, metabolic syndrome, and long-term cardiometabolic outcomes are still poorly defined.
My research focuses on whether post-treatment quantitative marrow MRI parameters independently predict recurrence, survival, or metabolic outcomes after adjusting for clinical covariates – whether marrow fat fraction changes mediate the relationship between treatment exposures and metabolic syndrome, and whether multiparametric MRI–radiomics models can improve risk prediction beyond traditional clinical factors.
This would allow the early identification of high-risk individuals using imaging biomarkers, and in turn, could enable preventive, tailored survivorship strategies.
Advances in quantitative MRI, radiomics, and computational modelling now make large-scale marrow phenotyping feasible, creating a unique opportunity to develop and validate MRI-based biomarkers that link marrow composition to metabolic and clinical outcomes, transforming risk assessment and long-term care for cancer survivors.
