Faculty Collaboration Database

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 2018 Jun;39(6):1008-1016

Date

05/26/2018

Pubmed ID

29794239

Pubmed Central ID

PMC6002911

DOI

10.3174/ajnr.A5675

Scopus ID

2-s2.0-85048696048 (requires institutional sign-in at Scopus site)   46 Citations

Abstract

BACKGROUND AND PURPOSE: Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors.

MATERIALS AND METHODS: DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity.

RESULTS: For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%).

CONCLUSIONS: By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.

Author List

Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC

Authors

Brent R. Logan PhD Director, Professor in the Data Science Institute department at Medical College of Wisconsin
Kathleen M. Schmainda PhD Professor in the Biophysics department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adult
Aged
Algorithms
Brain Neoplasms
Datasets as Topic
Female
Glioma
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
National Cancer Institute (U.S.)
United States