Share:
Share this content in WeChat
X
Clinical Article
Application value of DKI in distinguishing recurrence and pseudoprogression of glioma
DANG Pei  WANG Lidong  HUANG Xueying  LIU Jingjing  LÜ Ruirui  YANG Zhihua  WANG Xiaodong 

Cite this article as: Dang P, Wang LD, Huang XY, et al. Application value of DKI in distinguishing recurrence and pseudoprogression of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 28-33. DOI:10.12015/issn.1674-8034.2022.05.006.


[Abstract] Objective To investigate the value of DKI technology in differentiating glioma recurrence and pseudoprogression in clinical.Materials and Methods Retrospectively collect of 40 patients with glioma who underwent surgery, radiotherapy, chemotherapy and DKI scanning from October 2018 to December 2020 in the General Hospital of Ningxia Medical university. Patients was divided into the recurrence group (24 cases) and the pseudoprogression group (16 cases) by pathology or enhanced MRI scan followed up for more than 6 months. Data were compared by independent samples t-test, Mann-Whitney U-test and receiver operating characteristic to compare the DKI parameter values in enhancing lesions and peritumoral edema in the two groups of patients: Mean kurtosis (MK), mean diffusivity (MD), radial kurtosis (RK), axial kurtosis, fractional anisotropy. Using patient gression free survival (PFS) as the observation end point for events, cox proportional hazards model was used for multivariate analysis.Results Compared with the pseudoprogressive group, the ratio of MK (rMK) and ratio of RK (rRK) of the enhanced lesions in the recurrence group were increased, and ratio of MD (rMD) was decreased (P<0.05). The AUCs of rMK, rRK, and rMD were 0.94, 0.83, and 0.70, respectively (P<0.05). Compared with the pseudoprogressive group, the rMK of peritumoral edema was increased in the recurrence group and rMD was decreased (P<0.05). The area under the ROC curve of rMK and rMD were 0.82, 0.73, respectively (P<0.05). Involvement of the subventricular zone, rMK, rRK and rMD in enhanced lesions and rMK, rMD in peritumoral edema were correlated with PFS (P<0.05).Conclusions DKI can be used to distinguish recurrence and pseudoprogression of glioma, and the parameter value MK can be used as a better imaging marker, the MK value of enhancing lesions is an independent risk factor for PFS.
[Keywords] glioma;recurrence;pseudoprogression;diffusion kurtosis imaging;magnetic resonance imaging;peritumoral edema

DANG Pei1   WANG Lidong2   HUANG Xueying1   LIU Jingjing3   LÜ Ruirui4   YANG Zhihua5   WANG Xiaodong1*  

1 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750004, China

2 Department of Radiology, Yinchuan Traditional Chinese Medicine Hospital, Yinchuan 750001, China

3 Department of Radiology,Xi'an Chest Hospital,Xi'an 710061, China

4 Ningxia Medical University School of Clinical Medicine, Yinchuan 750004, China

5 Department of Radiotherapy, General Hospital of Ningxia Medical University, Yinchuan 750004, China

Wang XD, E-mail: xdw80@yeah.net

Conflicts of interest   None.

ACKNOWLEDGMENTS Key Research and Development Plans of Ningxia Hui Autonomous Region (2019BEG03037).
Received  2021-12-17
Accepted  2022-04-01
DOI: 10.12015/issn.1674-8034.2022.05.006
Cite this article as: Dang P, Wang LD, Huang XY, et al. Application value of DKI in distinguishing recurrence and pseudoprogression of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 28-33. DOI:10.12015/issn.1674-8034.2022.05.006.

[1]
Medical Administration and Hospital Administration Bureau of the National Health Commission. Guidelines for the diagnosis and treatment of glioma (2018 edition)[J]. Chin J Neurosurg, 2019, 35(3): 217-239. DOI: 10.3760/cma.j.issn.1001-2346.2019.03.001.
[2]
Strauss SB, Meng A, Ebani EJ, et al. Imaging glioblastoma posttreatment: progression, pseudoprogression, pseudoresponse, radiation necrosis[J]. Radiol Clin North Am, 2019, 57(6): 1199-1216. DOI: 10.1016/j.rcl.2019.07.003.
[3]
Zikou A, Sioka C, Alexiou GA, et al. Radiation necrosis, pseudoprogression, pseudoresponse, and tumor recurrence: imaging challenges for the evaluation of treated gliomas[J]. Contrast Media Mol Imaging, 2018, 2018: 6828396. DOI: 10.1155/2018/6828396.
[4]
Kruser TJ, Mehta MP, Robins HI. Pseudoprogression after glioma therapy: a comprehensive review[J]. Expert Rev Neurother, 2013, 13(4): 389-403. DOI: 10.1586/ern.13.7.
[5]
Lu VM, Jue TR, McDonald KL, et al. The survival effect of repeat surgery at glioblastoma recurrence and its trend: a systematic review and meta-analysis[J]. World Neurosurg, 2018, 115: 453-459.e3. DOI: 10.1016/j.wneu.2018.04.016.
[6]
Prager AJ, Martinez N, 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[J]. AJNR Am J Neuroradiol, 2015, 36(5): 877-885. DOI: 10.3174/ajnr.A4218.
[7]
Li Y, Ma YQ, Wu ZJ, et al. Advanced imaging techniques for differentiating pseudoprogression and tumor recurrence after immunotherapy for glioblastoma[J]. Front Immunol, 2021, 12: 790674. DOI: 10.3389/fimmu.2021.790674.
[8]
Ma B, Blakeley JO, Hong XH, et al. Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas[J]. J Magn Reson Imaging, 2016, 44(2): 456-462. DOI: 10.1002/jmri.25159.
[9]
Wang S, Martinez-Lage M, Sakai Y, et al. Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI[J]. AJNR Am J Neuroradiol, 2016, 37(1): 28-36. DOI: 10.3174/ajnr.A4474.
[10]
Guo ZX, Zhu XQ. The progress of applications of amide proton transfer[J]. J Med Imaging, 2021, 31(10): 1789-1792.
[11]
Pan F, Wu X, Su ZZ, et al. 3D-ASL and DWI in the differential diagnosis of recurrent glioma and pseudo progression[J]. J Clin Radiol, 2018, 37(6): 904-908. DOI: 10.13437/j.cnki.jcr.2018.06.003.
[12]
Takahashi S, Takahashi W, Tanaka S, et al. Radiomics analysis for glioma malignancy evaluation using diffusion kurtosis and tensor imaging[J]. Int J Radiat Oncol Biol Phys, 2019, 105(4): 784-791. DOI: 10.1016/j.ijrobp.2019.07.011.
[13]
Gao EY, Gao AK, Kit Kung W, et al. Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements[J]. Eur J Radiol, 2022, 147: 110104. DOI: 10.1016/j.ejrad.2021.110104.
[14]
Falk Delgado A, Nilsson M, van Westen D, et al. Glioma grade discrimination with MR diffusion kurtosis imaging: a meta-analysis of diagnostic accuracy[J]. Radiology, 2018, 287(1): 119-127. DOI: 10.1148/radiol.2017171315.
[15]
Zhao J, Wang YL, Li XB, 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]. J Neurooncol, 2019, 141(1): 195-203. DOI: 10.1007/s11060-018-03025-7.
[16]
Figini M, Riva M, Graham M, et al. Prediction of isocitrate dehydrogenase genotype in brain gliomas with MRI: single-shell versus multishell diffusion models[J]. Radiology, 2018, 289(3): 788-796. DOI: 10.1148/radiol.2018180054.
[17]
Wen PY, MacDonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group[J]. J Clin Oncol, 2010, 28(11): 1963-1972. DOI: 10.1200/jco.2009.26.3541.
[18]
Jiang HH, Yu KF, Li MX, et al. Classification of progression patterns in glioblastoma: analysis of predictive factors and clinical implications[J]. Front Oncol, 2020, 10: 590648. DOI: 10.3389/fonc.2020.590648.
[19]
Bai J, Cheng JL, Gao AK, et al. Ankang.Interpretation of the 2016 WHO classification of tumors of the central nervous system[J]. Chin J Radiol, 2016, 50(12): 1000-1005. DOI: 10.3760/cma.j.issn.1005-1201.2016.12.025.
[20]
Lee J, Wang N, Turk S, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning[J]. Sci Rep, 2020, 10(1): 20331. DOI: 10.1038/s41598-020-77389-0.
[21]
Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors[J]. J Magn Reson Imaging, 2018, 48(3): 571-589. DOI: 10.1002/jmri.26171.
[22]
Balaña C, Capellades J, Pineda E, et al. Pseudoprogression as an adverse event of glioblastoma therapy[J]. Cancer Med, 2017, 6(12): 2858-2866. DOI: 10.1002/cam4.1242.
[23]
Hempel JM, Brendle C, Bender B, et al. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study[J]. Eur J Radiol, 2019, 112: 144-152. DOI: 10.1016/j.ejrad.2019.01.014.
[24]
Wu XF, Liang X, Wang XC, et al. Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging[J]. Eur J Radiol, 2021, 135: 109445. DOI: 10.1016/j.ejrad.2020.109445.
[25]
Abdalla G, Dixon L, Sanverdi E, et al. The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis[J]. Neuroradiology, 2020, 62(7): 791-802. DOI: 10.1007/s00234-020-02425-9.
[26]
Li SR, Zheng Y, Sun WB, et al. Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging[J]. Eur Radiol, 2021, 31(11): 8197-8207. DOI: 10.1007/s00330-021-07959-x.
[27]
Kim MM, Umemura Y, Leung D. Bevacizumab and glioblastoma: past, present, and future directions[J]. Cancer J, 2018, 24(4): 180-186. DOI: 10.1097/PPO.0000000000000326.
[28]
Gao XY, Wang YD, Wu SM, et al. Differentiation of treatment-related effects from glioma recurrence using machine learning classifiers based upon pre-and post-contrast T1WI and T2 FLAIR subtraction features: a two-center study[J]. Cancer Manag Res, 2020, 12: 3191-3201. DOI: 10.2147/CMAR.S244262.
[29]
Lee J, Wang N, Turk S, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning[J]. Sci Rep, 2020, 10(1): 20331. DOI: 10.1038/s41598-020-77389-0.
[30]
Patel SH, Poisson LM, Brat DJ, et al. T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project[J]. Clin Cancer Res, 2017, 23(20): 6078-6085. DOI: 10.1158/1078-0432.CCR-17-0560.
[31]
Li FY, Shi WQ, Wang DW, et al. Evaluation of histopathological changes in the microstructure at the center and periphery of glioma tumors using diffusional kurtosis imaging[J]. Clin Neurol Neurosurg, 2016, 151: 120-127. DOI: 10.1016/j.clineuro.2016.10.018.
[32]
Ismail M, Hill V, Statsevych V, et al. Shape features of the lesion habitat to differentiate brain tumor progression from pseudoprogression on routine multiparametric MRI: a multisite study[J]. AJNR Am J Neuroradiol, 2018, 39(12): 2187-2193. DOI: 10.3174/ajnr.A5858.

PREV Deep gray matter changes in relapsing-remitting multiple sclerosis detected by multimodal MRI
NEXT Clinical application value of MR-based radiomics for differentiation of benign and malignant of parotid gland
  



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