K M Schmainda1, M A Prah2, S D Rand2,3, Y Liu4, B Logan4, M Muzi3, S D Rane2, X Da5, Y-F Yen6, J Kalpathy-Cramer6, T L Chenevert7, B Hoff7, B Ross7, Y Cao8, M P Aryal8, B Erickson9, P Korfiatis9, T Dondlinger10, L Bell11, L Hu12, P E Kinahan3, C C Quarles11. 1. From the Department of Radiology (K.M.S., M.A.P., S.D.R.) kathleen@mcw.edu. 2. From the Department of Radiology (K.M.S., M.A.P., S.D.R.). 3. Department of Radiology (M.M., S.D.R., P.E.K.), University of Washington, Seattle, Washington. 4. Division of Biostatistics (Y.L., B.L.), Institute for Health and Society, Medical College of Wisconsin, Milwaukee, Wisconsin. 5. Department of Radiology (X.D.), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts. 6. Athinoula A. Martinos Center for Biomedical Imaging (Y.-F.Y., J.K.-C.), Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Charlestown, Massachusetts. 7. Department of Radiology (T.L.C., B.H., B.R.). 8. Departments of Radiation Oncology, Radiology, and Biomedical Engineering (Y.C., M.P.A.), University of Michigan, Ann Arbor, Michigan. 9. Department of Radiology (B.E., P.K.), Mayo Clinic, Rochester, Minnesota. 10. Imaging Biometrics LLC (T.D.), Elm Grove, Wisconsin. 11. Division of Imaging Research (L.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona. 12. Department of Radiology (L.H.), Mayo Clinic, Scottsdale, Arizona.
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.
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.
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