The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment.
Methods
This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0–7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05.
Results
Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = −0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D∥, D∥10% and D∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = −0.73, −0.83, −0.71 and −0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008).
Conclusions
This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.
StuppRMasonWPvan den BentMJ, et al.Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med2005; 352: 987–996.
2.
CohenMHShenYLKeeganP, et al.FDA drug approval summary: bevacizumab (Avastin) as treatment of recurrent glioblastoma multiforme. Oncologist2009; 14: 1131–1138.
3.
FriedmanHSPradosMDWenPY, et al.Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol2009; 27: 4733–4740.
4.
KreislTNKimLMooreK, et al.Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. J Clin Oncol2009; 27: 740–745.
5.
NordenADYoungGSSetayeshK, et al.Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology2008; 70: 779–787.
6.
de GrootJFFullerGKumarAJ, et al.Tumor invasion after treatment of glioblastoma with bevacizumab: radiographic and pathologic correlation in humans and mice. Neuro Oncol2010; 12: 233–242.
7.
IwamotoFMAbreyLEBealK, et al.Patterns of relapse and prognosis after bevacizumab failure in recurrent glioblastoma. Neurology2009; 73: 1200–1206.
8.
WenPYMacdonaldDRReardonDA, et al.Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol2010; 28: 1963–1972.
9.
JainRK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science2005; 307: 58–62.
10.
BrandsmaDvan den BentMJ. Pseudoprogression and pseudoresponse in the treatment of gliomas. Curr Opin Neurol2009; 22: 633–638.
11.
Hygino da CruzLCJrRodriguezIDominguesRC, et al.Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol2011; 32: 1978–1985.
12.
PopeWBYoungJREllingsonBM. Advances in MRI assessment of gliomas and response to anti-VEGF therapy. Curr Neurol Neurosci Rep2011; 11: 336–344.
13.
KeunenOJohanssonMOudinA, et al.Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma. Proc Natl Acad Sci USA2011; 108: 3749–3754.
14.
NguyenHSMilbachNHurrellSL, et al.Progressing bevacizumab-induced diffusion restriction is associated with coagulative necrosis surrounded by viable tumor and decreased overall survival in patients with recurrent glioblastoma. AJNR Am J Neuroradiol2016; 37: 2201–2208.
15.
MongSEllingsonBMNghiemphuPL, et al.Persistent diffusion-restricted lesions in bevacizumab-treated malignant gliomas are associated with improved survival compared with matched controls. AJNR Am J Neuroradiol2012; 33: 1763–1770.
16.
LaViolettePSMickeviciusNJCochranEJ, et al.Precise ex vivo histological validation of heightened cellularity and diffusion-restricted necrosis in regions of dark apparent diffusion coefficient in 7 cases of high-grade glioma. Neuro Oncol2014; 16: 1599–1606.
17.
NowosielskiMRecheisWGoebelG, et al.ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma. Neuroradiology2011; 53: 291–302.
18.
JainRScarpaceLMEllikaS, et al.Imaging response criteria for recurrent gliomas treated with bevacizumab: role of diffusion weighted imaging as an imaging biomarker. J Neurooncol2010; 96: 423–431.
19.
PopeWBKimHJHuoJ, et al.Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology2009; 252: 182–189.
20.
PopeWBQiaoXJKimHJ, et al.Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neurooncol2012; 108: 491–498.
21.
McDonaldCRDelfantiRLKrishnanAP, et al.Restriction spectrum imaging predicts response to bevacizumab in patients with high-grade glioma. Neuro Oncol2016; 18: 1579–1590.
22.
EllingsonBMCloughesyTFLaiA, et al.Graded functional diffusion map-defined characteristics of apparent diffusion coefficients predict overall survival in recurrent glioblastoma treated with bevacizumab. Neuro Oncol2011; 13: 1151–1161.
23.
EllingsonBMKimEWoodworthDC, et al.Diffusion MRI quality control and functional diffusion map results in ACRIN 6677/RTOG 0625: a multicenter, randomized, phase II trial of bevacizumab and chemotherapy in recurrent glioblastoma. Int J Oncol2015; 46: 1883–1892.
24.
GerstnerERFroschMPBatchelorTT. Diffusion magnetic resonance imaging detects pathologically confirmed, nonenhancing tumor progression in a patient with recurrent glioblastoma receiving bevacizumab. J Clin Oncol2010; 28: e91–e93.
25.
GallaNChiangGChakrabortyS, et al.Apparent diffusion coefficient changes predict survival after intra-arterial bevacizumab treatment in recurrent glioblastoma. Neuroradiology2017; 59: 499–505.
26.
PaldinoMJDesjardinsAFriedmanHS, et al.A change in the apparent diffusion coefficient after treatment with bevacizumab is associated with decreased survival in patients with recurrent glioblastoma multiforme. Br J Radiol2012; 85: 382–389.
27.
RahmanRHamdanAZweiflerR, et al.Histogram analysis of apparent diffusion coefficient within enhancing and nonenhancing tumor volumes in recurrent glioblastoma patients treated with bevacizumab. J Neurooncol2014; 119: 149–158.
28.
ZhangMGulottaBThomasA, et al.Large-volume low apparent diffusion coefficient lesions predict poor survival in bevacizumab-treated glioblastoma patients. Neuro Oncol2016; 18: 735–743.
29.
HwangEJChaYLeeAL, et al.Early response evaluation for recurrent high grade gliomas treated with bevacizumab: a volumetric analysis using diffusion-weighted imaging. J Neurooncol2013; 112: 427–435.
30.
WenQJalilianLLupoJM, et al.Comparison of ADC metrics and their association with outcome for patients with newly diagnosed glioblastoma being treated with radiation therapy, temozolomide, erlotinib and bevacizumab. J Neurooncol2015; 121: 331–339.
31.
StadlbauerAGanslandtOBusleiR, et al.Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology2006; 240: 803–810.
32.
BeppuTInoueTShibataY, et al.Fractional anisotropy value by diffusion tensor magnetic resonance imaging as a predictor of cell density and proliferation activity of glioblastomas. Surg Neurol2005; 63: 56–61.
33.
KinoshitaMHashimotoNGotoT, et al.Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant brain tumors. Neuroimage2008; 43: 29–35.
34.
DengZYanYZhongD, et al.Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neurosci2010; 17: 1530–1536.
35.
WangKHaTChenX, et al.A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model. J Neurooncol2018; 137: 259–268.
36.
PriceSJJenaRBurnetNG, et al.Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol2006; 27: 1969–1974.
37.
RiegerJBahrOMullerK, et al.Bevacizumab-induced diffusion-restricted lesions in malignant glioma patients. J Neurooncol2010; 99: 49–56.
38.
GoyalPTenenbaumMGuptaS, et al.Survival prediction based on qualitative MRI diffusion signature in patients with recurrent high grade glioma treated with bevacizumab. Quant Imaging Med Surg2018; 8: 268–279.
39.
ZhaoJLiJBWangJY, et al.Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status. Neuroimage Clin2018; 19: 174–181.
40.
KothariPWhiteNSFaridN, et al.Longitudinal restriction spectrum imaging is resistant to pseudoresponse in patients with high-grade gliomas treated with bevacizumab. AJNR Am J Neuroradiol2013; 34: 1752–1757.
41.
RaabPHattingenEFranzK, et al.Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology2010; 254: 876–881.
42.
Van CauterSVeraartJSijbersJ, et al.Gliomas: diffusion kurtosis MR imaging in grading. Radiology2012; 263: 492–501.
43.
JensenJHHelpernJARamaniA, et al.Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med2005; 53: 1432–1440.
44.
JensenJHHelpernJA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed2010; 23: 698–710.
45.
BaiYLinYTianJ, et al.Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology2016; 278: 496–504.
46.
DelgadoAFFahlstromMNilssonM, et al.Diffusion kurtosis imaging of gliomas grades II and III – a study of perilesional tumor infiltration, tumor grades and subtypes at clinical presentation. Radiol Oncol2017; 51: 121–129.
47.
RajaRSinhaNSainiJ, et al.Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas. Neuroradiology2016; 58: 1217–1231.
48.
JiangRJiangJZhaoL, et al.Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget2015; 6: 42380–42393.
49.
LiFShiWWangD, et al.Evaluation of histopathological changes in the microstructure at the center and periphery of glioma tumors using diffusional kurtosis imaging. Clin Neurol Neurosurg2016; 151: 120–127.
50.
HempelJMBisdasSSchittenhelmJ, et al.In vivo molecular profiling of human glioma using diffusion kurtosis imaging. J Neurooncol2017; 131: 93–101.
51.
TietzeAHansenMBOstergaardL, et al.Mean diffusional kurtosis in patients with glioma: initial results with a fast imaging method in a clinical setting. AJNR Am J Neuroradiol2015; 36: 1472–1478.
52.
Falk DelgadoANilssonMvan WestenD, et al.Glioma grade discrimination with MR diffusion kurtosis imaging: a meta-analysis of diagnostic accuracy. Radiology2018; 287: 119–127.
53.
GoryawalaMZHerosDOKomotarRJ, et al.Value of diffusion kurtosis imaging in assessing low-grade gliomas. J Magn Reson Imaging2018; 48: 1551–1558.
54.
ZhaoJWangYLLiXB, et al.Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status. J Neurooncol2018; 141: 195–203.
55.
QiXXShiDFRenSX, et al.Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery. Eur Radiol2018; 28: 1748–1755.
56.
von ElmEAltmanDGEggerM, et al.Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ2007; 335: 806–808.
57.
MohantyVMcKinnonETHelpernJA, et al.Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain. Magn Reson Imaging2018; 48: 80–88.
58.
TabeshAJensenJHArdekaniBA, et al.Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med2011; 65: 823–836.
59.
BestDJRobertsDE. Algorithm AS 89: the upper tail probabilities of Spearman's rho. Appl Stat1975; 24: 377–379.
60.
FiellerECHartleyHOPearsonES. Tests for rank correlation coefficients. I. Biometrika1957; 44: 470–481.
61.
AuerTABreitHCMariniF, et al.Evaluation of the apparent diffusion coefficient in patients with recurrent glioblastoma under treatment with bevacizumab with radiographic pseudoresponse. J Neuroradiol2019; 46: 36–43.
62.
KunkelPUlbrichtUBohlenP, et al.Inhibition of glioma angiogenesis and growth in vivo by systemic treatment with a monoclonal antibody against vascular endothelial growth factor receptor-2. Cancer Res2001; 61: 6624–6628.
63.
RubensteinJLKimJOzawaT, et al.Anti-VEGF antibody treatment of glioblastoma prolongs survival but results in increased vascular cooption. Neoplasia2000; 2: 306–314.
64.
LuSAhnDJohnsonG, et al.Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol2003; 24: 937–941.
65.
LuSAhnDJohnsonG, et al.Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. Radiology2004; 232: 221–228.
66.
KimuraTOhkuboMIgarashiH, et al.Increase in glutamate as a sensitive indicator of extracellular matrix integrity in peritumoral edema: a 3.0-tesla proton magnetic resonance spectroscopy study. J Neurosurg2007; 106: 609–613.
67.
SternbergEJLiptonMLBurnsJ. Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors. AJNR Am J Neuroradiol2014; 35: 439–444.
68.
MoritaKMatsuzawaHFujiiY, et al.Diffusion tensor analysis of peritumoral edema using lambda chart analysis indicative of the heterogeneity of the microstructure within edema. J Neurosurg2005; 102: 336–341.
69.
ServerAGraffBAJosefsenR, et al.Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T. Eur J Radiol2014; 83: e156–e165.
70.
MinZGNiuCRanaN, et al.Differentiation of pure vasogenic edema and tumor-infiltrated edema in patients with peritumoral edema by analyzing the relationship of axial and radial diffusivities on 3.0T MRI. Clin Neurol Neurosurg2013; 115: 1366–1370.
71.
OlarAAldapeKD. Using the molecular classification of glioblastoma to inform personalized treatment. J Pathol2014; 232: 165–177.
72.
KarsyMNeilJAGuanJ, et al.A practical review of prognostic correlations of molecular biomarkers in glioblastoma. Neurosurg Focus2015; 38: E4.
73.
KesslerT. Predicting glioblastoma response to bevacizumab through marker profiling?Neuro Oncol2016; 18: 149–150.
74.
SathornsumeteeSCaoYMarcelloJE, et al.Tumor angiogenic and hypoxic profiles predict radiographic response and survival in malignant astrocytoma patients treated with bevacizumab and irinotecan. J Clin Oncol2008; 26: 271–278.
75.
AndersenCAstrupJGyldenstedC. Quantitation of peritumoural oedema and the effect of steroids using NMR-relaxation time imaging and blood–brain barrier analysis. Acta Neurochir Suppl (Wien)1994; 60: 413–415.
76.
ZakiHSJenkinsonMDDu PlessisDG, et al.Vanishing contrast enhancement in malignant glioma after corticosteroid treatment. Acta Neurochir (Wien)2004; 146: 841–845.
77.
MinamikawaSKonoKNakayamaK, et al.Glucocorticoid treatment of brain tumor patients: changes of apparent diffusion coefficient values measured by MR diffusion imaging. Neuroradiology2004; 46: 805–811.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
