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 Ktrans and Mean Kep 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 Ktrans and Kep along with tumor volume as essential parameters for differentiating true progression from treatment effects.
Basak et al. (Tue,) studied this question.