Share:
Share this content in WeChat
X
Experience Exchange
Value of diffusion-weighted imaging combined with diffusion kurtosis imaging in the hierarchical diagnosis and prognosis assessment of glioma
WANG Shaokai  HAN Xiangjun  ZHU Jingyi  ZHAO Yu  LI Songbai 

Cite this article as: Wang SK, Han XJ, Zhu JY, et al. Value of diffusion-weighted imaging combined with diffusion kurtosis imaging in the hierarchical diagnosis and prognosis assessment of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(9): 86-90, 99. DOI:10.12015/issn.1674-8034.2022.09.016.


[Abstract] Objective To investigate the value of diffusion-weighted imaging (DWI) combined with diffusion kurtosis imaging (DKI) in the hierarchical diagnosis and prognosis assessment of glioma.Materials and Methods A total of 82 cases with glioma who were admitted to our hospital from February 2017 to February 2019 were retrospectively analyzed, and all of them underwent DWI and DKI before surgery. The conventional MRI scan characteristics, DWI and DKI parameters, including apparent diffusion coefficient (ADC), mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), mean diffusivity (MD) and fractional anisotropy (FA) of patients with different grades of glioma were compared. The patients were followed up until October 2021. According to the prognosis, the patients were divided into the survival group and the death group, and the prognosis were analyzed by univariate analysis and multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve of DWI and DKI parameters predicting prognosis were drawn.Results Among the 82 glioma patients, 38 were low-grade (5 cases of grade 1, 33 cases of grade 2), and 44 were high grade (21 cases of grade 3, 23 cases of grade 4). There was no significant difference in the number of lesions, signal, lesion area, edema and enhancement in different grades of gliomas (P>0.05). With the increase of glioma grade, ADC and MD decreased, while MK, Ka, Kr increased (all P<0.05); the glioma grade was significantly negatively correlated with ADC and MD, and significantly positively correlated with Ka and Kr (all P<0.05). As of October 2021, 40 cases of the 82 glioma patients survived and 42 cases died. In the death group, the proportion of high-grade glioma, multiple lesions, obvious edema, and obvious enhancement, as well as MK, Ka, and Kr, were higher than those in the survival group, and ADC was lower than that in the survival group (P<0.05). Multivariate logistic regression analysis showed that glioma grade, peritumoral edema, tumor enhancement, ADC, and MK were prognostic factors (P<0.05). The area under the curve of MK for predicting the prognosis of glioma patients was 0.835 (95% CI: 0.690-0.961), and the sensitivity and specificity were 86.6% and 80.5% when 0.550 was used as the cut-off value; the area under the curve of ADC predicting the prognosis was 0.789 (95% CI: 0.633-0.945), the sensitivity and specificity were 82.9% and 76.8% when the cut-off value was 1.240; the area under the curve of MK combined with ADC for predicting prognosis was 0.903 (95% CI: 0.808-0.994), the sensitivity and specificity were 93.9% and 85.4%.Conclusions The DWI combined DKI can non-invasively evaluate the proliferation activity and water molecule diffusion information of glioma cells, and has a high evaluation value for the grading diagnosis and prognosis evaluation of glioma. The combination of MK and ADC can effectively predict the prognosis of glioma patients.
[Keywords] glioma;functional magnetic resonance imaging;diffusion-weighted imaging;diffusion kurtosis imaging;prognosis;predictive value

WANG Shaokai*   HAN Xiangjun   ZHU Jingyi   ZHAO Yu   LI Songbai  

Department of Radiology, the First Hospital of China Medical University, Shenyang 110001, China

*Wang SK, E-mail: wangshaokai678@163.com

Conflicts of interest   None.

Received  2022-04-29
Accepted  2022-08-05
DOI: 10.12015/issn.1674-8034.2022.09.016
Cite this article as: Wang SK, Han XJ, Zhu JY, et al. Value of diffusion-weighted imaging combined with diffusion kurtosis imaging in the hierarchical diagnosis and prognosis assessment of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(9): 86-90, 99.DOI:10.12015/issn.1674-8034.2022.09.016

[1]
Zhang XX, Li YH. Application progress of magnetic resonance technology in preoperative grading of glioma[J]. Chin Comput Med Imag, 2019, 25(3): 326-331. DOI: 10.19627/j.cnki.cn31-1700/th.2019.03.025.
[2]
Zhao H, Bai Y, Wang MY. Research progress of multimodal magnetic resonance imaging technology in glioma genotyping and prognosis assessment[J]. Chin J Magn Reson Imaging, 2021, 12(9): 5-8. DOI: 10.12015/issn.1674-8034.2021.09.025.
[3]
Zhe GG, Hao WJ. Research progress of magnetic resonance spectroscopy in boundary determination of glioma[J]. J Pract Oncol, 2019, 34(1): 7-10. DOI: 10.13267/j.cnki.syzlzz.2019.01.002.
[4]
Lan HF, Song XF, Chen B. Application of MRI in predicting molecular pathological classification of glioma[J]. J Pract Radiol, 2019, 35(8): 1206-1210. DOI: 10.3969/j.issn.1002-1671.2019.08.002.
[5]
Lin K, Cindan WJ, Qi Y, et al. Multimodal MR Quantitative and Qualitative Analysis for Grading of Glioma[J]. J Pract Radiol, 2019, 35(9): 1379-1383. DOI: 10.3969/j.issn.1002-1671.2019.09.001.
[6]
Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nerv-ousSystem:a summary[J]. Radiol Pract, 2021, 36(7): 818-831. DOI: 10.13609/j.cnki.1000-0313.2021.07.001.
[7]
Hu GJ, Hu XH, Liu DM, et al. The application value of functional imaging of motor network in the diagnosis and treatment of glioma[J]. Chin J Clin Neurosurg, 2021, 26(3):3-5. DOI: 10.13798/j.issn.1009-153X.2021.03.025.
[8]
Wang CP, Wang Y, Xiong F, et al. The value of apparent diffusion coefficient histogram in glioma grading[J]. J Pract Radiol, 2019, 35(1): 11-14. DOI: 10.3969/j.issn.1002-1671.2019.01.003.
[9]
Zhang L, Wen L, Zhang D, et al. The application of dynamic susceptibility contrast enhanced MR imaging in preoperative glioma grading and assessment of IDH mutation status[J]. J Mod Oncol, 2020, 28(21): 3779-3785. DOI: 10.3969/j.issn.1672-4992.2020.21.031.
[10]
Brennum J. What a waste of MRI-scans![J]. Acta Neurochir (Wien), 2019, 161(3): 567-568. DOI: 10.1007/s00701-018-03785-1.
[11]
Sollmann N. Structured reporting in neuro-oncological imaging: achieving reliable prediction of molecular subtypes in glioma based on pre-treatment multi-sequence MRI[J]. Eur Radiol, 2021, 31(10): 7371-7373. DOI: 10.1007/s00330-021-08210-3.
[12]
Ni WJ, Wang C, Ni DJ, et al. Retrospective analysis of the application value of fMRI and DTI in functional glioma surgery[J]. Oncoradiology, 2021, 30(2): 108-113. DOI: 10.19732/j.cnki.2096-6210.2021.02.009.
[13]
Hu XH, Liu HY. Application of functional imaging technology in surgical treatment of gliomas in functional areas[J]. J Clin Neurosurg, 2021, 18(5): 3-5. DOI: 10.3969/j.issn.1672-7770.2021.05.023.
[14]
Xie C, Duan YY, Wang XB, et al. The value of MR amide proton transfer weighted imaging technique in predicting the pathological grade of brainstem glioma[J]. Chin J Radiol, 2022, 56(2): 163-167. DOI: 10.3760/cma.j.cn112149-20210428-00419.
[15]
Park YW, Ahn SS, Moon JH, et al. Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging[J]. Neuroradiology, 2021, 63(11):1811-1822. DOI: 10.1007/s00234-021-02693-z.
[16]
Nabavizadeh SA, Ware JB, Wolf RL. Emerging Techniques in Imaging of Glioma Microenvironment[J]. Top Magn Reson Imaging, 2020, 29(2): 103-114. DOI: 10.1097/RMR.0000000000000232.
[17]
Verburg N, de Witt Hamer PC. State-of-the-art imaging for glioma surgery[J]. Neurosurg Rev, 2021, 44(3): 1331-1343. DOI: 10.1007/s10143-020-01337-9.
[18]
Lim-Fat MJ, Maralani PJ. Is there an optimal MRI surveillance schedule for patients with high-grade glioma after standard-of-care-therapy?[J]. Neuro Oncol, 2021, 23(5): 711-712. DOI: 10.1093/neuonc/noab053.
[19]
Edjlali M, Ploton L, Maurage CA, et al. Intraoperative MRI and FLAIR Analysis: Implications for low-grade glioma surgery[J]. J Neuroradiol, 2021, 48(1): 61-64. DOI: 10.1016/j.neurad.2019.08.005.
[20]
Leroy HA, Tuleasca C, Vannod-Michel Q, et al. Intraoperative MRI guidance for right deep fronto-temporal glioma resection: how I do it[J]. Acta Neurochir (Wien), 2020, 162(12): 3037-3041. DOI: 10.1007/s00701-020-04474-8.
[21]
Paprottka KJ, Kleiner S, Preibisch C, et al. Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression[J]. Eur J Nucl Med Mol Imaging, 2021, 48(13): 4445-4455. DOI: 10.1007/s00259-021-05427-8.
[22]
Lasocki A, Anjari M, Ӧrs Kokurcan S, et al. Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review[J]. Neuroradiology, 2021, 63(3): 353-362. DOI: 10.1007/s00234-020-02532-7.
[23]
Wang P, Weng L, Xie S, et al. Primary application of mean apparent propagator-MRI diffusion model in the grading of diffuse glioma[J/OL]. Eur J Radiol, 2021, 138 [2022-04-29]. https://linkinghub.elsevier.com/retrieve/pii/S0720048X21001029. DOI: 10.1016/j.ejrad.2021.109622.
[24]
Kim M, Jung SY, Park JE, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma[J]. Eur Radiol, 2020, 30(4): 2142-2151. DOI: 10.1007/s00330-019-06548-3.
[25]
Quan G, Zhang K, Liu Y, et al. Role of dynamic susceptibility contrast perfusion MRI in glioma progression evaluation[J/OL]. J Oncol, 2021 [2022-04-29]. https://www.hindawi.com/journals/jo/2021/1696387/. DOI: 10.1155/2021/1696387.
[26]
Booth TC, Williams M, Luis A, et al. Machine learning and glioma imaging biomarkers[J]. Clin Radiol, 2020, 75(1): 20-32. DOI: 10.1016/j.crad.2019.07.001.
[27]
Perrillat-Mercerot A, Guillevin C, Miranville A, et al. Using mathematics in MRI data management for glioma assesment[J]. J Neuroradiol, 2021, 48(4): 282-290. DOI: 10.1016/j.neurad.2019.11.004.
[28]
Zhuge Y, Ning H, Mathen P, et al. Automated glioma grading on conventional MRI images using deep convolutional neural networks[J]. Med Phys, 2020, 47(7): 3044-3053. DOI: 10.1002/mp.14168.
[29]
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.

PREV The value of diffusion kurtosis imaging in the early diagnosis of Parkinson,s disease
NEXT Tumor regression grade after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: MRI and pathological control study
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn