Differentiating True Progression from Pseudoprogression in Glioblastomas: Dynamic Contrast Enhanced-MRI Habitat Analysis with Parsimonious Pharmacokinetic Model

Background Distinguishing true progression (TP) from pseudoprogression (PsP) in glioblastoma (GBM) remains a clinical challenge as both often manifest as indeterminate contrast-enhancing lesions on post-treatment MRIs. However, integrating dynamic contrast-enhanced (DCE)-MRI with analytical methods such as clustering and machine learning offers a promising approach for distinguishing TP from PsP. Methods Quantitative analysis of DCE-MRI data from 82 patients with TP (n = 53) and PsP (n = 29) was performed using five pharmacokinetic models. Subsequently, a parsimonious selection method was used to identify the best fit for each voxel. In next step, clustering analysis using K-means ++ was performed to classify pixels into low, medium, and high-intensity regions. Tumors were segmented into enhancing, non-enhancing, and edema regions using automated nnU-Net algorithm and clusters were extracted from these regions. Significant features derived from clustering analysis and descriptive statistics (mean, median, and skewness) of DCE-MRI parameters and tumor volume were incorporated as input variables for classifier development. Finally, five machine learning classifiers were employed to differentiate between TP and PsP. Results Univariable clustering analysis of DCE-MRI data identified Ktrans, Ve, Vp, τi and tumor volumes as significant parameters for distinguishing TP from PsP. Multivariable clustering revealed that combined high-Ktrans–high-Ve is a key distinctive variable. Among machine learning classifiers tested using these parameters, Random Forest demonstrated the best diagnostic performance, achieving an accuracy of 83% and an AUC of 0.85. Conclusion Parsimonious analysis of DCE-MRI data in conjunction with clustering and machine learning could serve as a valuable method for accurately distinguishing TP from PsP in GBMs.