Identification of a Single-Dose, Low-Flip-Angle–Based CBV Threshold for Fractional Tumor Burden Mapping in Recurrent Glioblastoma

References

1. 1.↵
   1. Stupp R,
   2. Weller M,
   3. Belanger K, et al

   . Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987–96 doi:10.1056/NEJMoa043330

   CrossRefPubMedWeb of Science
2. 2.↵
   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
3. 3.↵
   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
4. 4.↵
   1. Ellingson BM,
   2. Chung C,
   3. Pope WB, et al

   . Pseudoprogression, radionecrosis, inflammation or true tumor progression? Challenges associated with glioblastoma response assessment in an evolving therapeutic landscape. J Neurooncol 2017;134:495–504 doi:10.1007/s11060-017-2375-2 pmid:28382534

   CrossRefPubMed
5. 5.↵
   1. Hygino Da Cruz LC,
   2. Rodriguez I,
   3. Domingues RC, et al

   . Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 2011;32:1978–85 doi:10.3174/ajnr.A2397 pmid:21393407

   Abstract/FREE Full Text
6. 6.↵
   1. Brandsma D,
   2. Stalpers L,
   3. Taal W, et al

   . Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 2008;9:453–61 doi:10.1016/S1470-2045(08)70125-6 pmid:18452856

   CrossRefPubMedWeb of Science
7. 7.↵
   1. Hu LS,
   2. Hawkins-Daarud A,
   3. Wang L, et al

   . Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett 2020;477:97–106 doi:10.1016/j.canlet.2020.02.025 pmid:32112907

   CrossRefPubMed
8. 8.↵
   1. Qin D,
   2. Yang G,
   3. Jing H, et al

   . Tumor progression and treatment-related changes: radiological diagnosis challenges for the evaluation of post treated glioma. Cancers (Basel) 2022;14:3771 doi:10.3390/cancers14153771

   CrossRefPubMed
9. 9.↵
   1. Thust SC,
   2. Van Den Bent MJ,
   3. Smits M

   . Pseudoprogression of brain tumors. J Magn Reson Imaging 2018;48:571–89 doi:10.1002/jmri.26171 pmid:29734497

   CrossRefPubMed
10. 10.↵
    1. Malik DG,
    2. Rath TJ,
    3. Urcuyo Acevedo JC, et al

    . Advanced MRI protocols to discriminate glioma from treatment effects: state of the art and future directions. Front Radio 2022;2:809373 doi:10.3389/fradi.2022.809373

    CrossRefPubMed
11. 11.↵
    1. Kim YH,
    2. Oh SW,
    3. Lim YJ, et al

    . Differentiating radiation necrosis from tumor recurrence in high-grade gliomas: assessing the efficacy of 18F-FDG PET, 11C-methionine PET and perfusion MRI. Clin Neurol Neurosurg 2010;112:758–65 doi:10.1016/j.clineuro.2010.06.005 pmid:20619531

    CrossRefPubMed
12. 12.↵
    1. Nael K,
    2. Bauer AH,
    3. Hormigo A, et al

    . Multiparametric MRI for differentiation of radiation necrosis from recurrent tumor in patients with treated glioblastoma. AJR Am J Roentgenol 2018;210:18–23 doi:10.2214/AJR.17.18003 pmid:28952810

    CrossRefPubMed
13. 13.↵
    1. Prager AJ,
    2. Martinez N,
    3. Beal K, et al

    . Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015;36:877–85 doi:10.3174/ajnr.A4218 pmid:25593202

    Abstract/FREE Full Text
14. 14.↵
    1. Zhang J,
    2. Wang Y,
    3. Wang Y, et al

    . Perfusion magnetic resonance imaging in the differentiation between glioma recurrence and pseudoprogression: a systematic review, meta-analysis and meta-regression. Quant Imaging Med Surg 2022;12:4805–22 doi:10.21037/qims-22-32 pmid:36185045

    CrossRefPubMed
15. 15.↵
    1. Sugahara T,
    2. Korogi Y,
    3. Tomiguchi S, et al

    . Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. AJNR Am J Neuroradiol 2000;21:901–09

    Abstract/FREE Full Text
16. 16.↵
    1. Wang L,
    2. Wei L,
    3. Wang J, et al

    . Evaluation of perfusion MRI value for tumor progression assessment after glioma radiotherapy: a systematic review and meta-analysis. Medicine (Baltimore) 2020;99:e23766 doi:10.1097/MD.0000000000023766 pmid:33350761

    CrossRefPubMed
17. 17.↵
    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:2219447

    CrossRefPubMed
18. 18.↵
    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
19. 19.↵
    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
20. 20.↵
    1. Gasparetto EL,
    2. Pawlak MA,
    3. Patel SH, et al

    . Posttreatment recurrence of malignant brain neoplasm: accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction. Radiology 2009;250:887–96 doi:10.1148/radiol.2502071444 pmid:19244052

    CrossRefPubMedWeb of Science
21. 21.↵
    1. Schmainda KM,
    2. Prah M,
    3. 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
22. 22.↵
    1. Law M,
    2. Young RJ,
    3. 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
23. 23.↵
    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. Magnetic Resonance in Med 2010;64:907–13 doi:10.1002/mrm.22445

    CrossRefPubMed
24. 24.↵
    1. Hoxworth JM,
    2. Eschbacher JM,
    3. Gonzales AC, et al

    . 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. AJNR Am J Neuroradiol 2020;41:408–15 doi:10.3174/ajnr.A6486 pmid:32165359

    Abstract/FREE Full Text
25. 25.↵
    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
26. 26.↵
    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
27. 27.↵
    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
28. 28.↵
    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
29. 29.↵
    1. Connelly JM,
    2. Prah MA,
    3. Santos-Pinheiro F, et al

    . Magnetic resonance imaging mapping of brain tumor burden: clinical implications for neurosurgical management: case report. Neurosurg Open 2021;2:okab029 doi:10.1093/neuopn/okab029 pmid:34661110

    CrossRefPubMed
30. 30.↵
    1. Amidon RF,
    2. Santos-Pinheiro F,
    3. Straza M, et al

    . Case report: fractional brain tumor burden magnetic resonance mapping to assess response to pulsed low-dose-rate radiotherapy in newly-diagnosed glioblastoma. Front Oncol 2022;12:1066191 doi:10.3389/fonc.2022.1066191 pmid:36561526

    CrossRefPubMed
31. 31.↵
    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
32. 32.↵
    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
33. 33.↵
    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
34. 34.↵
    1. Schmainda KM,
    2. Prah MA,
    3. 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
35. 35.↵
    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
36. 36.↵
    1. Welker K,
    2. Boxerman J,
    3. Kalnin A, et al

    . ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. AJNR Am J Neuroradiol 2015;36:E41–E51 doi:10.3174/ajnr.A4341 pmid:25907520

    Abstract/FREE Full Text
37. 37.↵
    1. Boxerman JL,
    2. Quarles CC,
    3. Hu LS, et al

    . Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 2020;22:1262–75 doi:10.1093/neuonc/noaa141 pmid:32516388

    CrossRefPubMed
38. 38.↵
    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

    PubMedWeb of Science
39. 39.↵
    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
40. 40.↵
    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
41. 41.↵
    1. Kuo F,
    2. Ng NN,
    3. Nagpal S, et al

    . DSC perfusion MRI-derived fractional tumor burden and relative CBV differentiate tumor progression and radiation necrosis in brain metastases treated with stereotactic radiosurgery. AJNR Am J Neuroradiol 2022;43:689–95 doi:10.3174/ajnr.A7501 pmid:35483909

    Abstract/FREE Full Text