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Journal of Medical Imaging • Open Access

Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression
Author(s): Zachary S. Kelm; Panagiotis D. Korfiatis; Ravi K. Lingineni; John R. Daniels; Jan C. Buckner; Daniel H. Lachance; Ian F. Parney; Rickey E. Carter; Bradley J. Erickson

Paper Abstract

Determining whether glioblastoma multiforme (GBM) is progressing despite treatment is challenging due to the pseudoprogression phenomenon seen on conventional MRIs, but relative cerebral blood volume (CBV) has been shown to be helpful. As CBV’s calculation from perfusion-weighted images is not standardized, we investigated whether there were differences between three FDA-cleared software packages in their CBV output values and subsequent performance regarding predicting survival/progression. Forty-five postradiation therapy GBM cases were retrospectively identified as having indeterminate MRI findings of progression versus pseudoprogression. The dynamic susceptibility contrast MR images were processed with different software and three different relative CBV metrics based on the abnormally enhancing regions were computed. The intersoftware intraclass correlation coefficients were 0.8 and below, depending on the metric used. No statistically significant difference in progression determination performance was found between the software packages, but performance was better for the cohort imaged at 3.0 T versus those imaged at 1.5 T for many relative CBV metric and classification criteria combinations. The results revealed clinically significant variation in relative CBV measures based on the software used, but minimal interoperator variation. We recommend against using specific relative CBV measurement thresholds for GBM progression determination unless the same software or processing algorithm is used.

Paper Details

Date Published: 26 May 2015
PDF: 14 pages
J. Med. Img. 2(2) 026001 doi: 10.1117/1.JMI.2.2.026001
Published in: Journal of Medical Imaging Volume 2, Issue 2
Show Author Affiliations
Zachary S. Kelm, Mayo Clinic (United States)
Panagiotis D. Korfiatis, Mayo Clinic (United States)
Ravi K. Lingineni, Mayo Clinic (United States)
John R. Daniels, Mayo Clinic Arizona (United States)
Jan C. Buckner, Mayo Clinic (United States)
Daniel H. Lachance, Mayo Clinic (United States)
Ian F. Parney, Mayo Clinic (United States)
Rickey E. Carter, Mayo Clinic (United States)
Bradley J. Erickson, Mayo Clinic (United States)


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