Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI

References

1. 1.↵
   2. Macdonald DR,
   3. Cascino TL,
   4. Schold SC Jr., et al

   . Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990;8:1277–80 pmid:2358840

   Abstract
2. 2.↵
   2. Wen PY,
   3. Macdonald DR,
   4. Reardon DA, et al

   . Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010;28:1963–72 doi:10.1200/JCO.2009.26.3541 pmid:20231676

   Abstract/FREE Full Text
3. 3.↵
   2. Topkan E,
   3. Topuk S,
   4. Oymak E, et al

   . Pseudoprogression in patients with glioblastoma multiforme after concurrent radiotherapy and temozolomide. Am J Clin Oncol 2012;35:284–89 doi:10.1097/COC.0b013e318210f54a pmid:21399487

   CrossRefPubMed
4. 4.↵
   2. Cha J,
   3. Kim ST,
   4. Kim HJ, et al

   . Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol 2014;35:1309–17 doi:10.3174/ajnr.A3876 pmid:24676005

   Abstract/FREE Full Text
5. 5.↵
   2. Chu HH,
   3. Choi SH,
   4. Ryoo I, et al

   . Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging. Radiology 2013;269:831–40 doi:10.1148/radiol.13122024 pmid:23771912

   CrossRefPubMed
6. 6.↵
   2. Lee WJ,
   3. Choi SH,
   4. Park CK, et al

   . Diffusion-weighted MR imaging for the differentiation of true progression from pseudoprogression following concomitant radiotherapy with temozolomide in patients with newly diagnosed high-grade gliomas. Acad Radiol 2012;19:1353–61 doi:10.1016/j.acra.2012.06.011 pmid:22884399

   CrossRefPubMed
7. 7.↵
   2. Suh CH,
   3. Kim HS,
   4. Choi YJ, et al

   . Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging. AJNR Am J Neuroradiol 2013;34:2278–86 doi:10.3174/ajnr.A3634 pmid:23828115

   Abstract/FREE Full Text
8. 8.↵
   2. Hamstra DA,
   3. Chenevert TL,
   4. Moffat BA, et al

   . Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A 2005;102:16759–64 doi:10.1073/pnas.0508347102 pmid:16267128

   Abstract/FREE Full Text
9. 9.↵
   2. Hygino da Cruz LC Jr.,
   3. Rodriguez I,
   4. 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
10. 10.↵
    2. Hu X,
    3. Wong KK,
    4. Young GS, et al

    . Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011;33:296–305 doi:10.1002/jmri.22432 pmid:21274970

    CrossRefPubMed
11. 11.↵
    2. Wang S,
    3. Kim SJ,
    4. Poptani H, et al

    . Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases. AJNR Am J Neuroradiol 2014;35:928–34 doi:10.3174/ajnr.A3871 pmid:24503556

    Abstract/FREE Full Text
12. 12.↵
    2. Wang S,
    3. Kim S,
    4. Chawla S, et al

    . Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011;32:507–14 doi:10.3174/ajnr.A2333 pmid:21330399

    Abstract/FREE Full Text
13. 13.↵
    2. Wang S,
    3. Kim S,
    4. Chawla S, et al

    . Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging. Neuroimage 2009;44:653–60 doi:10.1016/j.neuroimage.2008.09.027 pmid:18951985

    CrossRefPubMedWeb of Science
14. 14.↵
    2. Toh CH,
    3. Castillo M,
    4. Wong AM, et al

    . Primary cerebral lymphoma and glioblastoma multiforme: differences in diffusion characteristics evaluated with diffusion tensor imaging. AJNR Am J Neuroradiol 2008;29:471–75 doi:10.3174/ajnr.A0872 pmid:18065516

    Abstract/FREE Full Text
15. 15.↵
    2. Agarwal A,
    3. Kumar S,
    4. Narang J, et al

    . Morphologic MRI features, diffusion tensor imaging and radiation dosimetric analysis to differentiate pseudo-progression from early tumor progression. J Neurooncol 2013;112:413–20 doi:10.1007/s11060-013-1070-1 pmid:23417357

    CrossRefPubMed
16. 16.↵
    2. Lacerda S,
    3. Law M

    . Magnetic resonance perfusion and permeability imaging in brain tumors. Neuroimaging Clin N Am 2009;19:527–57 doi:10.1016/j.nic.2009.08.007 pmid:19959004

    CrossRefPubMedWeb of Science
17. 17.↵
    2. Gasparetto EL,
    3. Pawlak MA,
    4. 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
18. 18.↵
    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
19. 19.↵
    2. Tsien C,
    3. Galbán CJ,
    4. Chenevert TL, et al

    . Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol 2010;28:2293–99 doi:10.1200/JCO.2009.25.3971 pmid:20368564

    Abstract/FREE Full Text
20. 20.↵
    2. Kim HS,
    3. Kim JH,
    4. Kim SH, et al

    . Posttreatment high-grade glioma: usefulness of peak height position with semiquantitative MR perfusion histogram analysis in an entire contrast-enhanced lesion for predicting volume fraction of recurrence. Radiology 2010;256:906–15 doi:10.1148/radiol.10091461 pmid:20634429

    CrossRefPubMedWeb of Science
21. 21.↵
    2. Baek HJ,
    3. Kim HS,
    4. Kim N, et al

    . Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. Radiology 2012;264:834–43 doi:10.1148/radiol.12112120 pmid:22771885

    CrossRefPubMedWeb of Science
22. 22.↵
    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
23. 23.↵
    2. Chowdhary S,
    3. Chamberlain M

    . Bevacizumab for the treatment of glioblastoma. Expert Rev Neurother 2013;13:937–49 doi:10.1586/14737175.2013.827414 pmid:23952194

    CrossRefPubMedWeb of Science
24. 24.↵
    Tumor treating fields therapy for recurrent glioblastoma. Manag Care 2012;21:43–44 pmid:23304737

    PubMed
25. 25.↵
    2. Finocchiaro G,
    3. Pellegatta S

    . Perspectives for immunotherapy in glioblastoma treatment. Curr Opin Oncol 2014;26:608–14 doi:10.1097/CCO.0000000000000135 pmid:25210870

    CrossRefPubMed
26. 26.↵
    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
27. 27.↵
    2. Kim DY,
    3. Kim HS,
    4. Goh MJ, et al

    . Utility of intravoxel incoherent motion MR imaging for distinguishing recurrent metastatic tumor from treatment effect following gamma knife radiosurgery: initial experience. AJNR Am J Neuroradiol 2014;35:2082–90 doi:10.3174/ajnr.A3995 pmid:24970548

    Abstract/FREE Full Text
28. 28.↵
    2. Zamecnik J

    . The extracellular space and matrix of gliomas. Acta Neuropathol 2005;110:435–42 doi:10.1007/s00401-005-1078-5 pmid:16175354

    CrossRefPubMed
29. 29.↵
    2. Barajas RF Jr.,
    3. Chang JS,
    4. Segal MR, 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
30. 30.↵
    2. Pope WB,
    3. Lai A,
    4. Mehta R, et al

    . Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol 2011;32:882–89 doi:10.3174/ajnr.A2385 pmid:21330401

    Abstract/FREE Full Text
31. 31.↵
    2. Xu JL,
    3. Li YL,
    4. Lian JM, et al

    . Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology 2010;52:1193–99 doi:10.1007/s00234-010-0731-4 pmid:20571787

    CrossRefPubMedWeb of Science
32. 32.↵
    2. Jahangiri A,
    3. Aghi MK

    . Pseudoprogression and treatment effect. Neurosurg Clin N Am 2012;23:277–87, viii–ix doi:10.1016/j.nec.2012.01.002 pmid:22440871

    CrossRefPubMed
33. 33.↵
    2. Aquino D,
    3. Di Stefano AL,
    4. Scotti A, et al

    . Parametric response maps of perfusion MRI may identify recurrent glioblastomas responsive to bevacizumab and irinotecan. PLoS One 2014;9:e90535 doi:10.1371/journal.pone.0090535 pmid:24675671

    CrossRefPubMed