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Original Article
Multimodal MRI manifestations and pathological basis of peritumoral infiltration of glioma in rat
TU Yingshan  WENG Aiting  REN Anli  DONG Peng 

Cite this article as: TU Y S, WENG A T, REN A L, et al. Multimodal MRI manifestations and pathological basis of peritumoral infiltration of glioma in rat[J]. Chin J Magn Reson Imaging, 2023, 14(1): 105-110. DOI:10.12015/issn.1674-8034.2023.01.019.

[Abstract] Objective The characteristics of multimodal MRI in peritumoral infiltrating region of cerebral glioma and its correlation with corresponding pathological indicators were analyzed, so as to explore the molecularly biological basis of peritumoral infiltration in glioma.Materials and Methods A total of 32 female Wistar rats were selected as research subjects, which were injected C6 glioma cells to established rat glioma models in situ by micro sampling syringe. The control group was injected with the same amount of complete culture medium without glioma cells. The rats were examined with conventional MRI sequences, arterial spin labeling (ASL), diffusion weighted imaging (DWI), magnetic resonance spectrum (MRS) after 14 days. The relative apparent diffusion coefficient (rADC), Cho/NAA were measured and calculated, and the cerebral blood flow (CBF) was measured. At the same time, the expression of Ki-67, vascular endothelial growth factor (VEGF) and microvessel density (MVD) of CD105 were recorded. The correlation between the rADC value, CBF and Ki-67, VEGF, CD105-MVD were analyzed.Results (1) The differences in rADC, CBF, Ki-67, VEGF and CD105-MVD which between each two groups in the central region, the infiltrating region, the control group were statistically significant (P<0.05); (2) The Cho/NAA in the central area of glioma was higher than that in control group (P<0.05); (3) The rADC value were negatively correlated with Ki-67 in the central area of glioma and the infiltrating region (r=-0.92, -0.74); (4) The Cho/NAA was positively correlated with Ki-67 in the central area of glioma (r=0.76). There were positive correlations between the CBF and the expression of VEGF, CD105-MVD in the corresponding region (r=0.90, 0.72). The CBF in the infiltrating region had positive correlations with the expression of VEGF and CD105-MVD (r=0.90, 0.71).Conclusions This study revealed multimodal MRI was correlated with relative pathological indicators, and multimodal MRI was helpful to preliminary evaluate the molecularly biological characteristics in glioma and peritumoral infiltrating area, which may provide a certain basis for evaluation of tumoral scope and surgical excision.
[Keywords] cerebral glioma;peritumoral infiltration;magnetic resonance imaging;multimodal magnetic resonance imaging;arterial spin labeling;diffusion weighted imaging;magnetic resonance spectrum;rat;pathology

TU Yingshan1, 2   WENG Aiting2   REN Anli2   DONG Peng2*  

1 Department of Radiology, Fuzhou Second Hospital, Fuzhou, 350007, China

2 School of Medical Imaging, Weifang Medical University, Weifang 261053, China

Corresponding author: Dong P, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Shandong Province (No. ZR2014HL083).
Received  2022-06-22
Accepted  2022-11-29
DOI: 10.12015/issn.1674-8034.2023.01.019
Cite this article as: TU Y S, WENG A T, REN A L, et al. Multimodal MRI manifestations and pathological basis of peritumoral infiltration of glioma in rat[J]. Chin J Magn Reson Imaging, 2023, 14(1): 105-110. DOI:10.12015/issn.1674-8034.2023.01.019.

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.
LU H T, XING W, ZHANG Y W, et al. A Comparative Study of Histogram Parameters of Apparent Diffusion Coefficient with Histological Grade of Diffuse Gliomas and Ki-67 Proliferation Index[J]. Chinese Computed Medical Imaging, 2022, 28(1): 1-7. DOI: 10.19627/j.cnki.cn31-1700/th.2022.01.012.
PEKMEZCI M, MORSHED R A, CHUNDURU P, et al. Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology[J/OL]. Sci Rep, 2021, 11(1): 12162 [2022-06-22]. DOI: 10.1038/s41598-021-91648-8.
PANG H, DANG X, REN Y, et al. 3D-ASL perfusion correlates with VEGF expression and overall survival in glioma patients: Comparison of quantitative perfusion and pathology on accurate spatial location-matched basis[J]. J Magn Reson Imaging, 2019, 50(1): 209-220. DOI: 10.1002/jmri.26562
SHARMA U, JAGANNATHAN N R. Metabolism of prostate cancer by magnetic resonance spectroscopy (MRS)[J]. Biophys Rev, 2020, 12(5): 1163-1173. DOI: 10.1007/s12551-020-00758-6.
ZHAO H, BAI Y, WANG M Y. Progress of multimodality magnetic resonance imaging in genotyping and prognostic evaluation of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(9): 98-102. DOI: 10.12015/issn.1674-8034.2021.09.025.
XIAO G Q, WANG S T, XU F L. Value of DWI combined with MRS in glioma grade diagnosis[J]. Chin J Magn Reson Imaging, 2020, 11(7): 573-573. DOI: 10.12015/issn.1674-8034.2020.07.019.
WANG Q Q. Rat brain glioma peritumour infiltrating DWI/DTI and its pathological basic study[D]. Weifang: Weifang Medical University, 2018. DOI: 10.27736/d.cnki.gwfyx.2018.000001.
SALEHI RAVESH M, HUHNDORF M, MOUSSAVI A. Non-contrast enhanced molecular characterization of C6 rat glioma tumor at 7 T[J/OL]. Magn Reson Imaging, 2019, 61: 175-186 [2022-06-22]. DOI: 10.1016/j.mri.2019.05.036.
FÖRSTER A, BREHMER S, SEIZ-ROSENHAGEN M, et al. Heterogeneity of glioblastoma with gliomatosis cerebri growth pattern on diffusion and perfusion MRI[J]. J Neurooncol, 2019, 142(1): 103-109. DOI: 10.1007/s11060-018-03068-w.
SUI Z, ZHANG X, LI H, et al. Magnetic resonance imaging evaluation of brain glioma before postoperative radiotherapy[J]. Clin Transl Oncol, 2021, 23(4): 820-826. DOI: 10.1007/s12094-020-02474-9.
HU X, XUE M, SUN S, et al. Combined application of MRS and DWI can effectively predict cell proliferation and assess the grade of glioma: A prospective study[J/OL]. J Clin Neurosci, 2021, 83: 56-63 [2022-06-22]. DOI: 10.1016/j.jocn.2020.11.030.
YAN L, BAI Y. Application value of MRI multi-modal imaging in the diagnosis and grading of brain glioma[J]. Journal of Practical Medical Imaging, 2022, 23(2): 121-124. DOI: 10.16106/j.cnki.cn14-1281/r.2022.04.005.
TASO M, MUNSCH F, ZHAO L, et al. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL)[J]. J Cereb Blood Flow Metab, 2021, 41(8): 1899-1911. DOI: 10.1177/0271678X20982382.
ATA E S, TURGUT M, ERASLAN C, et al. Comparison between dynamic susceptibility contrast magnetic resonance imaging and arterial spin labeling techniques in distinguishing malignant from benign brain tumors[J]. Eur J Radiol, 2016, 85(9): 1545-1553. DOI: 10.1016/j.ejrad.2016.05.015.
DONG F, ZHANG P P, JIANG BW, et al. Quantitative evaluation of Ki-67 labeling index in glioma with transfer constant derived from dynamic contrast-enhanced MRI[J]. Chin J Radiol, 2017, 51(8): 568-568. DOI: 10.3760/cma.j.issn.1005-1201.2017.08.002.
SUN X, KAUFMAN P D. Ki-67: more than a proliferation marker. Chromosoma[J]. 2018, 127(2): 175-186. DOI: 10.1007/s00412-018-0659-8.
TEJADA S, BECERRA-CASTRO M V, NUÑEZ-CORDOBA J, et al. Ki-67 Proliferative Activity in the Tumor Margins as a Robust Prognosis Factor in Glioblastoma Patients[J]. J Neurol Surg A Cent Eur Neurosurg, 2021, 82(1): 53-58. DOI: 10.1055/s-0040-1709730.
WANG K, HA T, CHEN X, 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]. J Neurooncol, 2018, 137(2): 259-268. DOI: 10.1007/s11060-017-2734-z.
XU G, LI C, WANG Y, et al. Correlation between preoperative inflammatory markers, Ki-67 and the pathological grade of glioma[J/OL]. Medicine (Baltimore), 2021, 100(36): e26750 [2022-06-22]. DOI: 10.1097/MD.0000000000026750.
SHEN L, SUN R, KAN S, et al. EphA2, vascular endothelial growth factor, and vascular endothelial growth factor correlate with adverse outcomes and poor survival in patients with glioma[J/OL]. Medicine (Baltimore), 2021, 100(3): e23985 [2022-06-22]. DOI: 10.1097/MD.0000000000023985.
SEYEDMIRZAEI H, SHOBEIRI P, TURGUT M, et al. VEGF levels in patients with glioma: a systematic review and meta-analysis[J]. Rev Neurosci, 2020, 32(2): 191-202. DOI: 10.1515/revneuro-2020-0062.
AWASTHI R, RATHORE R K, SONI P, et al. Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers[J]. Neuroradiology, 2012, 54(3): 205-213. DOI: 10.1007/s00234-011-0874-y.
MIKKELSEN V E, STENSJØEN A L, GRANLI U S, et al. Angiogenesis and radiological tumor growth in patients with glioblastoma[J/OL]. BMC Cancer, 2018, 18(1): 862 [2022-06-22]. DOI: 10.1186/s12885-018-4768-9.
JIA Z Z, GU H M, ZHOU X J, et al. The assessment of immature microvascular density in brain gliomas with dynamic contrast-enhanced magnetic resonance imaging[J]. Eur J Radiol, 2015, 84(9): 1805-1809. DOI: 10.1016/j.ejrad.2015.05.035.
HUANG X, LIANG X, ZHANG Q, et al. Quantifying the angiogenesis of C6 glioma in rats based on CT quantitative parameters[J]. Acta Radiol, 2019, 60(8): 985-993. DOI: 10.1177/0284185118808073.
LIU C, YAN F, XU Y, et al. InVivo Molecular Ultrasound Assessment of Glioblastoma Neovasculature with Endoglin-Targeted Microbubbles[J/OL]. Contrast Media Mol Imaging, 2018, 2018: 8425495 [2022-06-22]. DOI: 10.1155/2018/8425495.
KANG Z P, WANG L X, LIU J, et al. Expression of CEACAM1 and CD105 in Renal Cell Carcinoma and Its Correlation with Microvessel Density[J]. Crit Rev Eukaryot Gene Expr, 2021, 31(1): 1-9. DOI: 10.1615/CritRevEukaryotGeneExpr.2020037168.
KONG X, WANG Y, LIU S, et al. CD105 Over-expression Is Associated with Higher WHO Grades for Gliomas[J]. Mol Neurobiol, 2016, 53(5): 3503-3512. DOI: 10.1007/s12035-015-9677-1.
KANG X W, XI Y B, LIU T T, et al. Grading of Glioma: combined diagnostic value of amide proton transfer weighted, arterial spin labeling and diffusion weighted magnetic resonance imaging[J/OL]. BMC Med Imaging, 2020, 20(1): 50 [2022-06-22]. DOI: 10.1186/s12880-020-00450-x.
KITIS O, ALTAY H, CALLI C, et al. Minimum apparent diffusion coefficients in the evaluation of brain tumors[J]. Eur J Radiol, 2005, 55(3): 393-400. DOI: 10.1016/j.ejrad.2005.02.004.
PARK Y W, AHN S S, PARK C J, et al. Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas[J]. Eur Radiol, 2020, 30(12): 6475-6484. DOI: 10.1007/s00330-020-07090-3.

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