Keywords: Cancer, biomarkers
Motivation: Non-parametric dynamic contrast-enhanced (DCE) MRI lacks a robust framework with which to quantify uncertainty in derived parameters.
Goal(s): To develop a framework for statistical treatment of non-parametric measures from DCE-MRI, such as area under the time-intensity curve (AUC) in soft-tissue sarcomas (STS).
Approach: Patients with limb STS were scanned using DCE-MRI prior to treatment. A Gaussian Process (radial basis kernel) was applied for voxel-wise signal modelling and prediction; all model parameters were optimized using full Hamiltonian Monte Carlo sampling.
Results: GPs effectively model time-varying DCE signals, reducing noise, quantifying non-parametric uncertainties, and enhancing visual quality of dynamic scans.
Impact: Gaussian Process modelling of DCE-MRI curves in soft-tissue sarcomas provides uncertainty quantification and reduces image noise, potentially enhancing the characterization of tumour heterogeneity. This approach may offer opportunities for predictive imaging and personalized treatment planning.
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