Moving Toward a Consensus DSC-MRI Protocol: Validation of a Low–Flip Angle Single-Dose Option as a Reference Standard for Brain Tumors

REFERENCES

1. 1.↵
   2. Donahue KM,
   3. Krouwer HGJ,
   4. Rand SD, et al

   . Utility of simultaneously acquired gradient-echo and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magn Reson Med 2000;43:845–53 doi:10.1002/1522-2594(200006)43:6<845::AID-MRM10>3.0.CO;2-J pmid:10861879

   CrossRefPubMedWeb of Science
2. 2.↵
   2. Schmainda KM,
   3. Rand SD,
   4. Joseph AM, et al

   . Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 2004;25:1524–32 pmid:15502131

   Abstract/FREE Full Text
3. 3.↵
   2. Kong DS,
   3. Kim ST,
   4. Kim EH, et al

   . Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status. AJNR Am J Neuroradiol 2011;32:382–87 doi:10.3174/ajnr.A2286 pmid:21252041

   Abstract/FREE Full Text
4. 4.↵
   2. Schmainda KM,
   3. Prah M,
   4. Connelly J, et al

   . Dynamic-susceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma. Neuro Oncol 2014;16:880–88 doi:10.1093/neuonc/not216 pmid:24431219

   CrossRefPubMed
5. 5.↵
   2. Schmainda KM,
   3. Zhang Z,
   4. Prah M, et al

   . Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial. Neuro Oncol 2015;17:1148–56 doi:10.1093/neuonc/nou364 pmid:25646027

   CrossRefPubMed
6. 6.↵
   2. Kickingereder P,
   3. Wiestler B,
   4. Burth S, et al

   . Relative cerebral blood volume is a potential predictive imaging biomarker of bevacizumab efficacy in recurrent glioblastoma. Neuro Oncol 2015;17:1139–47 doi:10.1093/neuonc/nov028 pmid:25754089

   CrossRefPubMed
7. 7.↵
   2. Hu LS,
   3. Eschbacher JM,
   4. Heiserman JE, et al

   . Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol 2012;14:919–30 doi:10.1093/neuonc/nos112 pmid:22561797

   CrossRefPubMed
8. 8.↵
   2. Prah MA,
   3. Al-Gizawiy MM,
   4. Mueller WM, et al

   . Spatial discrimination of glioblastoma and treatment effect with histologically-validated perfusion and diffusion magnetic resonance imaging metrics. J Neurooncol 2018;136:13–21 doi:10.1007/s11060-017-2617-3 pmid:28900832

   CrossRefPubMed
9. 9.↵
   2. Paulson ES,
   3. Schmainda KM

   . Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 2008;249:601–13 doi:10.1148/radiol.2492071659 pmid:18780827

   CrossRefPubMedWeb of Science
10. 10.↵
    2. Boxerman JL,
    3. Schmainda KM,
    4. Weisskoff RM

    . Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27:859–67 pmid:16611779

    Abstract/FREE Full Text
11. 11.↵
    2. Hu LS,
    3. Baxter LC,
    4. Pinnaduwage DS, et al

    . Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas. AJNR Am J Neuroradiol 2010;31:40–48 doi:10.3174/ajnr.A1787 pmid:19749223

    Abstract/FREE Full Text
12. 12.↵
    2. Boxerman JL,
    3. Prah DE,
    4. Paulson ES, et al

    . The role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard. AJNR Am J Neuroradiol 2012;33:1081–87 doi:10.3174/ajnr.A2934 pmid:22322605

    Abstract/FREE Full Text
13. 13.↵
    2. Schmainda KM,
    3. Prah MA,
    4. Rand SD, et al

    . 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;39:1008–16 doi:10.3174/ajnr.A5675 pmid:29794239

    Abstract/FREE Full Text
14. 14.↵
    2. Boxerman JL,
    3. Shiroishi MS,
    4. Ellingson BM, et al

    . Dynamic susceptibility contrast MR imaging in glioma: review of current clinical practice. Magn Reson Imaging Clin N Am 2016;24:649–70 doi:10.1016/j.mric.2016.06.005 pmid:27742108

    CrossRefPubMed
15. 15.↵
    2. Ellingson BM,
    3. Bendszus M,
    4. Boxerman J, et al

    ; Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol 2015;17:1188–98 doi:10.1093/neuonc/nov095 pmid:26250565

    CrossRefPubMed
16. 16.↵
    2. Leu K,
    3. Boxerman JL,
    4. Ellingson BM

    . Effects of MRI protocol parameters, preload injection dose, fractionation strategies, and leakage correction algorithms on the fidelity of dynamic-susceptibility contrast MRI estimates of relative cerebral blood volume in gliomas. AJNR Am J Neuroradiol 2017;38:478–84 doi:10.3174/ajnr.A5027 pmid:28034995

    Abstract/FREE Full Text
17. 17.↵
    2. Semmineh NB,
    3. Bell LC,
    4. Stokes AM, et al

    . Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI using a population-based digital reference object. AJNR Am J Neuroradiol 2018;39:1981–88 doi:10.3174/ajnr.A5827 pmid:30309842

    Abstract/FREE Full Text
18. 18.↵
    2. Bedekar D,
    3. Jensen TR,
    4. Schmainda KM

    . Standardization of relative cerebral blood volume (rCBV) image maps for ease of both inter- and intrapatient comparisons. Magn Reson Med 2010;64:907–13 doi:10.1002/mrm.22445 pmid:20806381

    CrossRefPubMed
19. 19.↵
    2. Nyúl LG,
    3. Udupa JK

    . On standardizing the MR image intensity scale. Magn Reson Med 1999;42:1072–81 doi:10.1002/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M pmid:10571928

    CrossRefPubMed
20. 20.↵
    2. Prah MA,
    3. Stufflebeam SM,
    4. Paulson ES, et al

    . Repeatability of standardized and normalized relative CBV in patients with newly diagnosed glioblastoma. AJNR Am J Neuroradiol 2015;36:1654–61 doi:10.3174/ajnr.A4374 pmid:26066626

    Abstract/FREE Full Text
21. 21.↵
    2. Bedekar D,
    3. Jensen T,
    4. Rand S, et al

    . Delta T1 Method: an automatic post-contrast RO1 selection technique for brain tumors. In: Proceedings of the International Society for Magnetic Resonance in Medicine, Stockholm, Sweden. May 1–7, 2010
22. 22.↵
    2. Altman DG

    . Practical Statistics for Medical Research. London: Chapman and Hall/CRC Texts in Statistical Science Series, Taylor & Francis; 1990
23. 23.↵
    2. McBride GB

    . A proposal for strength-of-agreeement criteria for Lin's concordance correlation coefficient. NIWA Client Report: HAM2005–062 2005:62
24. 24.↵
    2. Semmineh NB,
    3. Stokes AM,
    4. Bell LC, et al

    . A population-based digital reference object (DRO) for optimizing dynamic susceptibility contrast (DSC)-MRI methods for clinical trials. Tomography 2017;3:41–49 doi:10.18383/j.tom.2016.00286 pmid:28584878

    CrossRefPubMed
25. 25.↵
    2. Oh J,
    3. Henry RG,
    4. Pirzkall A, et al

    . Survival analysis in patients with glioblastoma multiforme: predictive value of choline-to-N-acetylaspartate index, apparent diffusion coefficient, and relative cerebral blood volume. J Magn Reson Imaging 2004;19:546–54 doi:10.1002/jmri.20039 pmid:15112303

    CrossRefPubMed
26. 26.↵
    2. Lev MH,
    3. Ozsunar Y,
    4. Henson JW, et al

    . Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 2004;25:214–21 pmid:14970020

    Abstract/FREE Full Text
27. 27.↵
    2. Aronen HJ,
    3. Gazit IE,
    4. Louis DN, et al

    . Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 1994;191:41–51 doi:10.1148/radiology.191.1.8134596 pmid:8134596

    CrossRefPubMedWeb of Science
28. 28.↵
    2. Schmiedeskamp H,
    3. Straka M,
    4. Newbould RD, et al

    . Combined spin- and gradient-echo perfusion-weighted imaging. Magn Reson Med 2012;68:30–40 doi:10.1002/mrm.23195 pmid:22114040

    CrossRefPubMed
29. 29.↵
    2. Law M,
    3. Young RJ,
    4. Babb JS, et al

    . Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2008;247:490–98 doi:10.1148/radiol.2472070898 pmid:18349315

    CrossRefPubMedWeb of Science
30. 30.↵
    2. Cha S,
    3. Lu S,
    4. Johnson G, et al

    . Dynamic susceptibility contrast MR imaging: correlation of signal intensity changes with cerebral blood volume measurements. J Magn Reson Imaging 2000;11:114–19 doi:10.1002/(SICI)1522-2586(200002)11:2<114::AID-JMRI6>3.0.CO;2-S pmid:10713942

    CrossRefPubMed
31. 31.↵
    2. Harris RJ,
    3. Cloughesy TF,
    4. Hardy AJ, et al

    . MRI perfusion measurements calculated using advanced deconvolution techniques predict survival in recurrent glioblastoma treated with bevacizumab. J Neurooncol 2015;122:497–505 doi:10.1007/s11060-015-1755-8 pmid:25773062

    CrossRefPubMed
32. 32.↵
    2. Hu LS,
    3. Eschbacher JM,
    4. Dueck AC, et al

    . Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma. AJNR Am J Neuroradiol 2012;33:69–76 doi:10.3174/ajnr.A2743 pmid:22095961

    Abstract/FREE Full Text
33. 33.↵
    2. Surapaneni K,
    3. Kennedy BC,
    4. Yanagihara TK, et al

    . Early cerebral blood volume changes predict progression after convection-enhanced delivery of topotecan for recurrent malignant glioma. World Neurosurg 2015;84:163–72 doi:10.1016/j.wneu.2015.03.008 pmid:25772608

    CrossRefPubMed
34. 34.↵
    2. Mangla R,
    3. Singh G,
    4. Ziegelitz D, et al

    . Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology 2010;256:575–84 doi:10.1148/radiol.10091440 pmid:20529987

    CrossRefPubMedWeb of Science
35. 35.↵
    2. Prince MR,
    3. Zhang HL,
    4. Roditi GH, et al

    . Risk factors for NSF: a literature review. J Magn Reson Imaging 2009;30:1298–308 doi:10.1002/jmri.21973 pmid:19937930

    CrossRefPubMedWeb of Science
36. 36.↵
    2. McDonald RJ,
    3. McDonald JS,
    4. Kallmes DF, et al

    . Intracranial gadolinium deposition after contrast-enhanced MR imaging. Radiology 2015;275:772–82 doi:10.1148/radiol.15150025 pmid:25742194

    CrossRefPubMed
37. 37.↵
    2. Cha S

    . Perfusion MR imaging: basic principles and clinical applications. Magn Reson Imaging Clin N Am 2003;11:403–13 doi:10.1016/S1064-9689(03)00066-7 pmid:14768726

    CrossRefPubMed
38. 38.↵
    2. Law M,
    3. Cha S,
    4. Knopp EA, et al

    . High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 2002;222:715–21 doi:10.1148/radiol.2223010558 pmid:11867790

    CrossRefPubMedWeb of Science
39. 39.↵
    2. Law M,
    3. Oh S,
    4. Babb J, et al

    . Cerebral blood volume predicts patient outcome better than histopathology in low-grade gliomas using dynamic susceptibility contrast-perfusion MR imaging. In: Proceedings of the Annual Meeting of the American Society of Neuroradiology, Toronto, Ontario, Canada. May 23–27, 2005:488