Abstract #0068

Clustering-Based Multiparametric MRI for Differentiation Between True Tumor Progression and Pseudoprogression in Glioblastoma

Sourav Basak¹, Gabriela W Kostrzanowska², Subhanon Bera^1,3, Archith Rajan⁴, Sanjeev Chawla⁴, Harish Poptani², and Sourav Bhaduri^1,3

¹Institute for Advancing Intelligence (IAI), TCG Centres of Research and Education in Science and Technology (TCG CREST), Kolkata, India, ²Centre for Preclinical Imaging, University of Liverpool, Liverpool, United Kingdom, ³Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India, ⁴Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

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

Motivation: Clustering analysis in brain tumor aims to improve differentiation between true progression (TP) and pseudoprogression (PsP) in glioblastoma using multiparametric MRI-based pharmacokinetic and diffusion parameters, addressing a critical diagnostic challenge to enhance treatment assessment and planning.

Goal(s): The objective is to distinguish between true progression (TP) from pseudoprogression (PsP) in glioblastoma using multiparametric MRI-based clustering analysis.

Approach: K-means ++ Clustering based Multiparametric MRI data analysis for segregating the tumor into low and high intensity regions.

Results: Mean K^trans and Mean K_ep are statistically significant DCE-MRI parameters for differentiating true progression from pseudoprogression of glioblastoma.

Impact: These results demonstrate the importance of vascular permeability in tumor assessment and establish K^trans and K_epalong with tumor volume as essential parameters for differentiating true progression from treatment effects.

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