Document Type : Original Research

Authors

• Mohammad Ghorbani ^{1, 2}
• Mohammad Ali Oghabian ^{1, 2}
• Samira Raminfard ^{2, 3}
• Maryam Farsi ³
• Nahid Sadighi ^{3, 4}
• Mostafa Farzin ^{5, 6}

¹ Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

² Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute, Tehran University of Medical Sciences, Tehran, Iran

³ Medical Imaging Center of Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), Tehran, Iran

⁴ Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Complex Hospital, Tehran University of Medical Sciences, Tehran, Iran

⁵ Department of Radiation Oncology, Cancer Institute, IKHC, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

⁶ Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Science, Tehran, Iran

Abstract

Background: Differentiating pseudoprogression (PsP) from true progression (TP) in high-grade gliomas (HGGs) is challenging, as conventional Magnetic Resonance Imaging (MRI) lacks sufficient specificity. Intravoxel incoherent motion (IVIM) MRI, may improve diagnostic performance by providing insights into tumor microstructure.
Objective: To evaluate the diagnostic performance of IVIM MRI derived parameters histogram in distinguishing PsP from TP in HGG patients.
Material and Methods: In a prospective study, 30 patients with WHO grade III or IV gliomas, previously treated with standard therapy, underwent IVIM MRI. Parametric maps (D, D*, f, and Apparent Diffusion Coefficient (ADC)) were normalized to contralateral white matter, and histogram features were extracted from enhancing lesions. Univariate analysis identified significant features, and multivariate logistic regression assessed their combined diagnostic performance. The model’s diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, with Area Under Curve (AUC), sensitivity, specificity, and accuracy.
Results: Histogram analysis revealed significant differences in most histogram features of the D-ratio, D*-ratio, and ADC-ratio between PsP and TP groups. The 50^th percentile of the D-ratio and the 99^th percentile of the D*-ratio were identified as independent predictors in the final model, with AUC values of 0.79 and 0.728, respectively. The final model achieved an AUC of 0.853, demonstrating high sensitivity (93.8%), specificity (64.3%), and overall accuracy (80%), outperforming individual parameters. 
Conclusion: The 50^th percentile of the D-ratio and the 99^th percentile of the D*-ratio demonstrated significant discrimination power between PsP and TP. Their combination further enhanced diagnostic accuracy, making them valuable metrics for clinical decision-making in HGG management.

Keywords

• Diffusion Magnetic Resonance Imaging
• Glioma
• Brain Neoplasms
• Intravoxel incoherent motion MRI
• IVIM MRI
• Pseudoprogression