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CLINICAL ARTICLE
The diagnostic value of IVIM in glioma grading and its correlation with Ki-67 labeling index
CHANG Tianjing  SHEN Huicong  YU Meimei  GE Ying  ZHANG Yang  ZHANG Zhe  JI Nan 

Cite this article as: Chang TJ, Shen HC, Yu MM, et al. The diagnostic value of IVIM in glioma grading and its correlation with Ki-67 labeling index[J]. Chin J Magn Reson Imaging, 2021, 12(2): 19-23. DOI:10.12015/issn.1674-8034.2021.02.005.


[Abstract] Objective To investigate the value of introvoxel incoherent motion (IVIM) parameters in predicting preoperative grade of glioma and its correlation with Ki-67 labeling index. Materials andMethods Sixty-three patients (43 males and 20 females), aged from 16 to 74 (47±13) years, were retrospectively analyzed. They were divided into low-grade group (30 cases of WHO grade II) and high-grade group (33 cases of WHO grade III and IV). Ki-67 labeling index was obtained by immunohistochemistry. All patients underwent routine MRI scan and IVIM-DWI examination before operation. The IVIM parameters of the largest solid area and contralateral normal white matter area were measured, and the apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion related diffusion coefficient (D*) and perfusion fraction (f) were obtained. The measured value of solid area was divided by the value of contralateral normal brain parenchymal area. The corrected parameters were obtained: relative ADC (relative ADC, rADC), relative D (relative D, rD), relative D* (relative D*, rD*), relative f (relative f, rf). Rank sum test (Mann Whitney U test) was used to compare the differences of four quantitative parameters and Ki-67 between high and low-grade groups. Spearman method was used to analyze the correlation between the four quantitative parameters and Ki-67 LI. Receiver operator characteristic curve (ROC) was used to evaluate the diagnostic efficacy of four quantitative parameters in the grading of glioma.Results The rADC, rD and rf values in low-grade glioma group were higher than those in high-grade glioma group (P<0.05). There was no significant difference in rD* between the two groups (P=0.139). The average Ki-67 labeling index in high-grade glioma group was significantly higher than that in low-grade glioma group (P<0.01).The area under the ROC curve of rADC, rD and rf values were 0.912, 0.911 and 0.714, the thresholds were 1.280, 1.295 and 1.171, the sensitivities were 93.3%, 90.0%, 86.7%, and the specificities were 75.8%, 78.8% and 48.5%. There was a strong negative correlation between rADC, rD and Ki-67 labeling index, a low correlation between rD* and Ki-67 labeling index, but no significant correlation between rf and Ki-67 labeling index.Conclusions IVIM can noninvasively evaluate the grade of glioma, among which rADC has the highest diagnostic efficiency. Ki-67 labeling index was significantly different between high-grade and low-grade gliomas, and had a strong negative correlation with rADC and rD, which could provide help for the clinical diagnosis, formulation of treatment plan and prognosis judgment of glioma.
[Keywords] magnetic resonance imaging;glioma;introvoxel incoherent motion imaging;Ki-67 labeling index;tumor grading

CHANG Tianjing1   SHEN Huicong1*   YU Meimei1   GE Ying1   ZHANG Yang2   ZHANG Zhe2   JI Nan2  

1 Department of Radiology, Beijing Tiantan Hospital Affiliated to Capital Medical University, Beijing 100070, China

2 Department of Neurosurgery, Beijing Tiantan Hospital Affiliated to Capital Medical University, Beijing 100070, China

Shen HC, E-mail: shenhuicong@126.com

Conflicts of interest   None.

ACKNOWLEDGENTS This work was part of Beijing Municipal Science and Technology Project (No.Z181100001718196).
Received  2020-09-22
Accepted  2020-11-15
DOI: 10.12015/issn.1674-8034.2021.02.005
Cite this article as: Chang TJ, Shen HC, Yu MM, et al. The diagnostic value of IVIM in glioma grading and its correlation with Ki-67 labeling index[J]. Chin J Magn Reson Imaging, 2021, 12(2): 19-23. DOI:10.12015/issn.1674-8034.2021.02.005.

1
Tonoyan AS, Pronin IN, Pitshelauri DI, et al. A correlation between diffusion kurtosis imaging and the proliferative activity of brain glioma[J]. Zh Vopr Neirokhir Im N N Burdenko, 2015, 79(6): 5-14. DOI: 10.17116/neiro20157965-14.
2
Saksena S, Jain R, Narang J, et al. Predicting survival in glioblastomas using diffusion tensor imaging metrics[J]. J Magn Reson Imaging, 2010, 32(4): 788-795. DOI: 10.1002/jmri.22304.
3
Togao O, Hiwatashi A, Yamashita K, et al. Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging[J]. Neuro-Oncology, 2016, 18 (1): 132-141. DOI: 10.1093/neuonc/nov147.
4
Miyoshi F, Shinohara Y, Kambe A, et al. Utility of intravoxel incoherent motion magnetic resonance imaging and arterial spin labeling for recurrent glioma after bevacizumab treatment[J]. Acta Radiologica, 2018, 59(11): 1372-1379. DOI: 10.1177/0284185118759707.
5
De la Fuente MI, Young RJ, Rubel J, et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma[J]. Neuro-Oncology, 2016, 18(2): 283-290.
6
Suo S, Cheng F, Cao M, et al. Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors[J]. J Magn Reson Imaging, 2017, 46(3): 740-750. DOI: 10.1002/jmri.25612.
7
Li YH, Lu JP, Duan XJ, et al. A preliminary study of pediatric brain tumors using multi-component diffusion weighted imaging[J]. Radiol Prac, 2012, 27(2): 159-163. DOI: 10.13609/j.cnki.1000-0313.2012.02.013.
8
Le Bihan D, Breton E, Lallemand D, et al. MR imaging of incoherent motion: application to diffusion and perfusion in neurologic disorders[J]. Radiology, 1986, 161(2): 401-407.
9
Hao FL, Wu H, Niu GM. Application of single index, double index and stretch index model DWI in preoperative classifcation of gliomas[J]. Chin J Magn Reson Imaging, 2019, 10(6): 401-405.
10
Tietze A, Choi C, Mickey B, et al. Noninvasive assessment of isocitrate dehydrogenase mutation status in cerebral gliomas by magnetic resonance spectroscopy in a clinical setting[J]. J Neurosurgery, 2018, 128(2): 391-398. DOI: 10.3171/2016.10.JNS161793.
11
Li W, Niu C, Shakir TM, et al. An evidence-based approach to assess the accuracy of intravoxel incoherent motion imaging for the grading of brain tumors[J]. Medicine, 2018, 97(45): e13217. DOI: 10.1097/MD.0000000000013217.
12
Peng LR, Kong QC, Jiang T. Correlation between apparent diffusion coefficient values and histopathological grading of cerebral gliomas[J]. Chin J Neuromed, 2017, 16(10): 1041-1045. DOI: 10.3760/cma.j.issn.1671-8925.2017.10.013.
13
Bai Y, Lin Y, Tian J, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging[J]. Radiology, 2016, 278(2): 496-504. DOI: 10.1148/radiol.2015142173.
14
Shen N, Zhao L, Jiang J, et al. Intravoxel incoherent motion diffusion-weighted imaging analysis of diffusion and microperfusion in grading gliomas and comparison with arterial spin labeling for evaluation of tumor perfusion[J]. J Magn Reson Imaging, 2016, 44(3): 620-632. DOI: 10.1002/jmri.25191.
15
Lin Y, Li J, Zhang Z, et al. Comparison of intravoxel incoherent motion diffusion-weighted MR imaging and arterial spin labeling MR imaging in gliomas[J]. Biomed Res Int, 2015, 2015: 234-245.
16
Hu YC, Yan LF, Wu L, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: efficacy in preoperative grading[J]. Sci Rep, 2014, 4: 7208. DOI: 10.1038/srep07208.
17
Lee JT, Liau J, Murphy P, et al. Cross-sectional investigation of correlation between hepatic steatosis and IVIM perfusion on MR imaging[J]. Magn Reson Imaging, 2012, 30(4): 572-578. DOI: 10.1016/j.mri.2011.12.013.
18
Reifenberger G, Hentschel B, Felsberg J, et al. Predictive impact of MGMT promoter methylation in glioblastoma of the elderly[J]. Int J Cancer, 2012, 131(6): 1342-1350. DOI: 10.1002/ijc.27385.
19
Christian F, Milena C, Marion R, et al. Mosimann, philippe maeder, reto meuli, max wintermark. IVIM perfusion fraction is prognostic for survival in brain glioma[J]. Clin Neuroradiol, 2017, 27(4): 485-492. DOI: 10.1007/s00062-016-0510-7.
20
Yan R, Haopeng P, Xiaoyuan F, et al. Non-gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index[J]. Neuroradiology, 2016, 58(2): 121-132. DOI: 10.1007/s00234-015-1606-5.
21
Tian BW, Yang GQ, Qin JB, et al. Application value of IVIM in predicting MGMT promoter methylation status in high-grade gliomas[J]. Chin J Magn Reson Imaging, 2020, 11(7): 506-510. DOI: 10.12015/issn.1674-8034.2020.07.006.
22
Jiang R, Jiang J, Zhao L, et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation[J]. Oncotarget, 2015, 39(6): 42380-42393. DOI: 10.18632/oncotarget.5675.
23
Fudaba H, Shimomura T, Abe T, et al. Comparison of multiple parameters obtained on 3.0 T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading[J]. AJNR, 2014, 35(11): 2091-2098. DOI: 10.3174/ajnr.A4018.

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