Repeatability of Cerebral Perfusion Using Dynamic Susceptibility Contrast MRI in Glioblastoma Patients

doi:10.1016/j.tranon.2015.03.002

Translational Oncology

Volume 8, Issue 3, June 2015, Pages 137-146

https://doi.org/10.1016/j.tranon.2015.03.002 Get rights and content

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Abstract

OBJECTIVES

This study evaluates the repeatability of brain perfusion using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) with a variety of post-processing methods.

METHODS

Thirty-two patients with newly diagnosed glioblastoma were recruited. On a 3-T MRI using a dual-echo, gradient-echo spin-echo DSC-MRI protocol, the patients were scanned twice 1 to 5 days apart. Perfusion maps including cerebral blood volume (CBV) and cerebral blood flow (CBF) were generated using two contrast agent leakage correction methods, along with testing normalization to reference tissue, and application of arterial input function (AIF). Repeatability of CBV and CBF within tumor regions and healthy tissues, identified by structural images, was assessed with intra-class correlation coefficients (ICCs) and repeatability coefficients (RCs). Coefficients of variation (CVs) were reported for selected methods.

RESULTS

CBV and CBF were highly repeatable within tumor with ICC values up to 0.97. However, both CBV and CBF showed lower ICCs for healthy cortical tissues (up to 0.83), healthy gray matter (up to 0.95), and healthy white matter (WM; up to 0.93). The values of CV ranged from 6% to 10% in tumor and 3% to 11% in healthy tissues. The values of RC relative to the mean value of measurement within healthy WM ranged from 22% to 42% in tumor and 7% to 43% in healthy tissues. These percentages show how much variation in perfusion parameter, relative to that in healthy WM, we expect to observe to consider it statistically significant. We also found that normalization improved repeatability, but AIF deconvolution did not.

CONCLUSIONS

DSC-MRI is highly repeatable in high-grade glioma patients.

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¹: This work was supported by NCI/NIH R21CA117079, R01CA129371, and K24CA125440 (T.B.); NIH S10RR023401, 5R01NS069696, and 5R01NS060918 (S.M.S.); Saic-Frederick Inc. grant 26XS263 (T.B.); Norwegian Research Council grant 191088/V50 and Norwegian Cancer Society Grant 3434180 (K.E.E.); Harvard Catalyst grant M01-RR-01066 (T.B.); and NIH Awards UL1 RR025758 (T.B. and B.R.) and R01NS059775 (O.W.); Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Research Resources and the National Center for Advancing Translational Sciences, NIH Award UL1 TR001102) and financial contributions from Harvard University and its affiliated academic healthcare centers. This work also involved the use of instrumentation supported by the NCRR Shared Instrumentation Grant Program (1S10RR023401 and 1S10RR023043). Disclosure/conflict of interest: K.E.E. and A.B. have intellectual property rights at NordicNeuroLab AS. A.B. is a board member at NordicNeuroLab. K.S. is a co-founder of Imaging Biometrics LLC, a company that develops medical image processing software. She and her family have equity interest in, receive income from, and own intellectual property being developed. P.Y.W. is on Advisory Boards for Genentech and Novartis. T.B., Pharmaceutical consulting: 1) Merck & Co., Inc., 2) Roche, 3) Kirin Pharmaceuticals; CME lectures/material: 1) Up to Date, Inc., 2) Robert Michael Educational Institute LLC, 3) Educational Concepts Group, 4) Research to Practice, 5) Oakstone Medical, 6) Publishing, 7) American Society of Hematology; other consulting: 1) Champions, 2) Biotechnology, 3) Advance Medical; research support: 1) Pfizer, 2) Astra Zeneca, 3) Millennium. B.R. is on the consultant-advisory board of Siemens Medical.

²: This article refers to supplementary materials, which are designated by Tables S1 to S3 and Figures S1 and S2 and are available online at www.transonc.com.