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Abstract #0073

Region-wise Comparison of DCE-MRI based Pharmacokinetic Model Fitting in Glioblastoma

Subhanon Bera1,2, Sourav Basak1, Sankar Narayan Misra1,2, Gabriela W Kostrzanowska3, Archith Rajan4, Sanjeev Chawla4, Harish Poptani3, and Sourav Bhaduri1,2
1Institute for Advancing Intelligence (IAI), TCG Centres for Research and Education in Science and Technology (TCG CREST), KOLKATA, India, 2Academy of Scientific and Innovative Research ( AcSIR), Ghaziabad, India, 3Centre for Preclinical Imaging, University of Liverpool, Liverpool, United Kingdom, 4Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

Keywords: Tumors (Post-Treatment), DSC & DCE Perfusion

Motivation: Glioblastoma (GBM) has poor survival rates, and DCE-MRI aids in understanding tumor vascularity. However, selecting an optimal pharmacokinetic (PK) model remains challenging.

Goal(s): To identify the best-fitting PK model for GBM regions using an Akaike Information Criterion (AIC)-based parsimonious model selection.

Approach: DCE-MRI data from 46 GBM patients were segmented into enhancing, non-enhancing, and edema regions. Five PK models were fitted per region, with AIC used to select the best model per voxel. A parsimonious model was created from these best-fits.

Results: The parsimonious model outperformed individual models across regions, with Extended Tofts, 2CXM and Shutter-Speed models excelling in enhancing/non-enhancing regions and edema.

Impact: This study improves GBM assessment by optimizing DCE-MRI PK model selection. It tailors models to specific tumor regions, enabling more accurate quantification of tumor characteristics, aiding diagnosis & treatment planning. This approach can be applied to other tumors and imaging protocols.

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