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Abstract #2068

Gaussian Processes for Enhancing DCE Imaging Quality and Probabilistic Interpretation of Non-Parametric Biomarkers in Soft-Tissue Sarcomas

Yifan Guo1, Imogen Thrussell1,2, Megan Morris1, Jessica M Winfield1,2, David J Collins1,2, Navita Somaiah1,3, Dow-Mu Koh1,2, Nicola Rosenfelder4, Christina Messiou1,2, and Matthew D Blackledge1,2
1Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom, 2MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom, 3Breast Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom, 4Neuro-oncology Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom

Synopsis

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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