Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies

References

1. 1.↵
   1. Shiroishi MS,
   2. Boxerman JL,
   3. Pope WB

   . Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma. Neuro Oncol 2016;18:467–78 doi:10.1093/neuonc/nov179 pmid:26364321

   CrossRefPubMed
2. 2.↵
   1. Brandsma D,
   2. van den Bent MJ

   . Pseudoprogression and pseudoresponse in the treatment of gliomas. Curr Opin Neurol 2009;22:633–38 doi:10.1097/WCO.0b013e328332363e pmid:19770760

   CrossRefPubMed
3. 3.↵
   1. Fink J,
   2. Born D,
   3. Chamberlain MC

   . Pseudoprogression: relevance with respect to treatment of high-grade gliomas. Curr Treat Options Oncol 2011;12:240–52 doi:10.1007/s11864-011-0157-1 pmid:21594589

   CrossRefPubMed
4. 4.↵
   1. Clarke JL,
   2. Chang S

   . Pseudoprogression and pseudoresponse: challenges in brain tumor imaging. Curr Neurol Neurosci Rep 2009;9:241–46 doi:10.1007/s11910-009-0035-4 pmid:19348713

   CrossRefPubMedWeb of Science
5. 5.↵
   1. Hu LS,
   2. Eschbacher JM,
   3. 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
6. 6.↵
   1. Kim JH,
   2. Bae Kim Y,
   3. Han JH, et al

   . Pathologic diagnosis of recurrent glioblastoma: morphologic, immunohistochemical, and molecular analysis of 20 paired cases. Am J Surg Pathol 2012;36:620–28 doi:10.1097/PAS.0b013e318246040c pmid:22441548

   CrossRefPubMed
7. 7.↵
   1. Forsyth PA,
   2. Kelly PJ,
   3. Cascino TL, et al

   . Radiation necrosis or glioma recurrence: is computer-assisted stereotactic biopsy useful? J Neurosurg 1995;82:436–44 doi:10.3171/jns.1995.82.3.0436 pmid:7861222

   CrossRefPubMedWeb of Science
8. 8.↵
   1. Rock JP,
   2. Hearshen D,
   3. Scarpace L, et al

   . Correlations between magnetic resonance spectroscopy and image-guided histopathology, with special attention to radiation necrosis. Neurosurgery 2002;51:912–19; discussion 919–20 doi:10.1227/00006123-200210000-00010 pmid:12234397

   CrossRefPubMedWeb of Science
9. 9.↵
   Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008;455:1061–68 doi:10.1038/nature07385 pmid:18772890

   CrossRefPubMedWeb of Science
10. 10.↵
    1. Patel P,
    2. Baradaran H,
    3. Delgado D, et al

    . MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol 2017;19:118–27 doi:10.1093/neuonc/now148 pmid:27502247

    CrossRefPubMed
11. 11.↵
    1. Prah MA,
    2. Al-Gizawiy MM,
    3. 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
12. 12.↵
    1. Barajas RF,
    2. Chang JS,
    3. Segal MR Jr., et al

    . Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2009;253:486–96 doi:10.1148/radiol.2532090007 pmid:19789240

    CrossRefPubMedWeb of Science
13. 13.↵
    1. Hu LS,
    2. Baxter LC,
    3. Smith KA, et al

    . Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. AJNR Am J Neuroradiol 2009;30:552–58 doi:10.3174/ajnr.A1377 pmid:19056837

    Abstract/FREE Full Text
14. 14.↵
    1. Fatterpekar GM,
    2. Galheigo D,
    3. Narayana A, et al

    . Treatment-related change versus tumor recurrence in high-grade gliomas: a diagnostic conundrum—use of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI. AJR Am J Roentgenol 2012;198:19–26 doi:10.2214/AJR.11.7417 pmid:22194475

    CrossRefPubMed
15. 15.↵
    1. Welker K,
    2. Boxerman J,
    3. Kalnin A, et al

    ; American Society of Functional Neuroradiology MR Perfusion Standards and Practice Subcommittee of the ASFNR Clinical Practice Committee. ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. AJNR Am J Neuroradiol 2015;36:E41–51 doi:10.3174/ajnr.A4341 pmid:25907520

    Abstract/FREE Full Text
16. 16.↵
    1. Semmineh NB,
    2. Bell LC,
    3. 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
17. 17.↵
    1. Leu K,
    2. Boxerman JL,
    3. 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
18. 18.↵
    1. Hu LS,
    2. Kelm Z,
    3. Korfiatis P, et al

    . Impact of software modeling on the accuracy of perfusion MRI in glioma. AJNR Am J Neuroradiol 2015;36:2242–49 doi:10.3174/ajnr.A4451 pmid:26359151

    Abstract/FREE Full Text
19. 19.↵
    1. Kelm ZS,
    2. Korfiatis PD,
    3. Lingineni RK, et al

    . Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression. J Med Imaging (Bellingham) 2015;2:026001 doi:10.1117/1.JMI.2.2.026001 pmid:26158114

    CrossRefPubMed
20. 20.↵
    1. Hu LS,
    2. Baxter LC,
    3. 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
21. 21.↵
    1. Boxerman JL,
    2. Schmainda KM,
    3. 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
22. 22.↵
    1. Bedekar D,
    2. Jensen T,
    3. 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
23. 23.↵
    1. Prah MA,
    2. Stufflebeam SM,
    3. 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
24. 24.↵
    1. Smits M,
    2. Bendszus M,
    3. Collette S, et al

    . Repeatability and reproducibility of relative cerebral blood volume measurement of recurrent glioma in a multicentre trial setting. Eur J Cancer 2019;114:89–96 doi:10.1016/j.ejca.2019.03.007 pmid:31078973

    CrossRefPubMed
25. 25.↵
    1. Stupp R,
    2. Mason WP,
    3. van den Bent MJ, et al

    ; European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987–96 doi:10.1056/NEJMoa043330 pmid:15758009

    CrossRefPubMedWeb of Science
26. 26.↵
    1. Louis DN,
    2. Perry A,
    3. Reifenberger G, et al

    . The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016;131:803–20 doi:10.1007/s00401-016-1545-1 pmid:27157931

    CrossRefPubMed
27. 27.↵
    1. Hu LS,
    2. Ning S,
    3. Eschbacher JM, et al

    . Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One 2015;10:e0141506 doi:10.1371/journal.pone.0141506 pmid:26599106

    CrossRefPubMed
28. 28.↵
    1. Hu LS,
    2. Ning S,
    3. Eschbacher JM, et al

    . Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 2017;19:128–37 doi:10.1093/neuonc/now135 pmid:27502248

    CrossRefPubMed
29. 29.↵
    1. Hu LS,
    2. Eschbacher JM,
    3. 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
30. 30.↵
    1. Paulson ES,
    2. 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
31. 31.↵
    1. Kassner A,
    2. Annesley DJ,
    3. Zhu XP, et al

    . Abnormalities of the contrast re-circulation phase in cerebral tumors demonstrated using dynamic susceptibility contrast-enhanced imaging: a possible marker of vascular tortuosity. J Magn Reson Imaging 2000;11:103–13 doi:10.1002/(SICI)1522-2586(200002)11:2<103::AID-JMRI5>3.0.CO;2-Z pmid:10713941

    CrossRefPubMed
32. 32.↵
    1. Nyul LG,
    2. Udupa JK

    . Approach to standardizing MR image intensity scale. In: Proceedings of the SPIE 1999;3658:595–603 doi:10.1117/12.349472
33. 33.↵
    1. Burger PC,
    2. Mahley MS Jr.,
    3. Dudka L, et al

    . The morphologic effects of radiation administered therapeutically for intracranial gliomas: a postmortem study of 25 cases. Cancer 1979;44:1256–72 doi:10.1002/1097-0142(197910)44:4<1256::AID-CNCR2820440415>3.0.CO;2-T pmid:387205

    CrossRefPubMedWeb of Science
34. 34.↵
    1. Bell LC,
    2. Stokes AM,
    3. Quarles CC

    . Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)-MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T. J Magn Reson Imaging 2020;51:547–53 doi:10.1002/jmri.26837 pmid:31206948

    CrossRefPubMed
35. 35.↵
    1. Schmainda KM,
    2. Prah MA,
    3. Hu LS, et al

    . Moving toward a consensus DSC-MRI protocol: validation of a low-flip angle single-dose option as a reference standard for brain tumors. AJNR Am J Neuroradiol 2019;40:626–33 doi:10.3174/ajnr.A6015 pmid:30923088

    Abstract/FREE Full Text
36. 36.↵
    1. Iv M,
    2. Liu X,
    3. Lavezo J, et al

    . Perfusion MRI-based fractional tumor burden differentiates between tumor and treatment effect in recurrent glioblastomas and informs clinical decision-making. AJNR Am J Neuroradiol 2019;40:1649–57 doi:10.3174/ajnr.A6211 pmid:31515215

    Abstract/FREE Full Text
37. 37.↵
    1. Muruganandham M,
    2. Lupu M,
    3. Dyke JP, et al

    . Preclinical evaluation of tumor microvascular response to a novel antiangiogenic/antitumor agent RO0281501 by dynamic contrast-enhanced MRI at 1.5 T. Mol Cancer Ther 2006;5:1950–57 doi:10.1158/1535-7163.MCT-06-0010 pmid:16928815

    Abstract/FREE Full Text
38. 38.↵
    1. Wilmes LJ,
    2. Pallavicini MG,
    3. Fleming LM, et al

    . AG-013736, a novel inhibitor of VEGF receptor tyrosine kinases, inhibits breast cancer growth and decreases vascular permeability as detected by dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Imaging 2007;25:319–27 doi:10.1016/j.mri.2006.09.041 pmid:17371720

    CrossRefPubMed
39. 39.↵
    1. Doblas S,
    2. He T,
    3. Saunders D, et al

    . Glioma morphology and tumor-induced vascular alterations revealed in seven rodent glioma models by in vivo magnetic resonance imaging and angiography. J Magn Reson Imaging 2010;32:267–75 doi:10.1002/jmri.22263 pmid:20677250

    CrossRefPubMed
40. 40.↵
    1. Schmainda KM,
    2. Zhang Z,
    3. 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
41. 41.↵
    1. Gerstner ER,
    2. Zhang Z,
    3. Fink JR, et al

    . ACRIN 6684: Assessment of Tumor Hypoxia in Newly Diagnosed Glioblastoma Using 18F-FMISO PET and MRI. Clin Cancer Res 2016;22:5079–86 doi:10.1158/1078-0432.CCR-15-2529 pmid:27185374

    Abstract/FREE Full Text
42. 42.↵
    1. Barajas RF,
    2. Phillips JJ,
    3. Parvataneni R Jr., et al

    . Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR imaging. Neuro Oncol 2012;14:942–54 doi:10.1093/neuonc/nos128 pmid:22711606

    CrossRefPubMed
43. 43.↵
    1. Stadlbauer A,
    2. Ganslandt O,
    3. Buslei R, et al

    . Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 2006;240:803–10 doi:10.1148/radiol.2403050937 pmid:16926329

    CrossRefPubMedWeb of Science