Related Experiment Video
Updated: Mar 1, 2026
Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
Differentiating enhancing multiple sclerosis lesions, glioblastoma, and lymphoma with dynamic texture parameters
Rajeev Kumar Verma1,2, Roland Wiest1, Cäcilia Locher1
1Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, 3010, Switzerland.
Purpose:
MR-imaging hallmarks of glioblastoma (GB), cerebral lymphoma (CL), and demyelinating lesions are gadolinium (Gd) uptake due to blood-brain barrier disruption. Thus, initial diagnosis may be difficult based on conventional Gd-enhanced MRI alone. Here, the added value of a dynamic texture parameter analysis (DTPA) in the differentiation between these three entities is examined. DTPA is an in-house software tool that incorporates the analysis of quantitative texture parameters extracted from dynamic susceptibility contrast-enhanced (DSCE) images.
Methods:
Twelve patients with multiple sclerosis (MS), 15 patients with GB, and five patients with CL were included. The image analysis method focuses on the DSCE image time series during bolus passage. Three time intervals were examined: inflow, outflow, and reperfusion time interval. Texture maps were computed. From the DSCE image series, mean, difference, standard deviation, and variance texture parameters were calculated and statistically analyzed and compared between the pathologies.
Results:
The texture parameters of the original DSCE image series for mean, standard deviation, and variance showed the most significant differences (P-value between <0.00 and 0.05) between pathologies. Further, the texture parameters related to the standard deviation or variance (both associated with tissue heterogeneity) revealed the strongest discriminations between the pathologies.
Conclusion:
We conclude that dynamic perfusion texture parameters as assessed by the DTPA method allow discriminating MS, GB, and CL lesions during the first passage of contrast. DTPA used in combination with classification algorithms has the potential to find the most likely diagnosis given a postulated differential diagnosis.
