In short, Alexander Leeman's research entails the following:
Harmonization and segmentation of multicenter paediatric sarcoma MRI data through artificial intelligence to support clinical decision making in international trials
'Rhabdomyosarcoma and Ewing sarcoma are rare tumours with a prevalence of around 35 cases per year in the Dutch paediatric and adolescent population. Currently, there are no biomarkers that can distinguish between poor and good outcomes at an early stage of treatment of these tumours. While microstructural tissue properties derived from diffusion MRI (dMRI) could be valuable biomarkers for early assessment of treatment efficacy, differences in scanner hardware and acquisition protocols across sites complicate dMRI data pooling necessary for clinical decision making in international trials. In this project, we will develop artificial intelligence (AI) methodology for harmonizing dMRI signals and segmenting tumour MRI data that can alleviate unwanted multicenter variability while preserving the biological signature of tissue microstructure.'
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