Differentiating Enhancing Multiple Sclerosis Lesions, Glioblastoma, and Lymphoma with Dynamic Texture Parameters Analysis (DTPA) - a Feasibility Study.

Verma, Rajeev Kumar; Wiest, Roland; Locher, C; Heldner, Mirjam Rachel; Schucht, Philippe; Raabe, Andreas; Gralla, Jan; Kamm, Christian Philipp; Slotboom, Johannes; Kellner-Weldon, Frauke (2017). Differentiating Enhancing Multiple Sclerosis Lesions, Glioblastoma, and Lymphoma with Dynamic Texture Parameters Analysis (DTPA) - a Feasibility Study. Medical physics, 44(8), pp. 4000-4008. American Association of Physicists in Medicine AAPM 10.1002/mp.12356

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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), fifteen 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 have the potential to find the most likely diagnosis given a postulated differential diagnosis. This article is protected by copyright. All rights reserved.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery

UniBE Contributor:

Verma, Rajeev Kumar; Wiest, Roland; Heldner, Mirjam Rachel; Schucht, Philippe; Raabe, Andreas; Gralla, Jan; Kamm, Christian Philipp; Slotboom, Johannes and Kellner-Weldon, Frauke

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0094-2405

Publisher:

American Association of Physicists in Medicine AAPM

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

08 Aug 2017 09:18

Last Modified:

08 Aug 2017 09:18

Publisher DOI:

10.1002/mp.12356

PubMed ID:

28543071

Uncontrolled Keywords:

cerebral lymphoma; dynamic susceptibility contrast-enhanced perfusion imaging; dynamic texture parameter analysis; glioblastoma; multiple sclerosis

URI:

https://boris.unibe.ch/id/eprint/101633

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