Share:
Share this content in WeChat
X
CLINICAL ARTICLE
The clinical value of T1 and T2 values in predicting brain glioma grading and cell proliferation activity
XIE Jiapei  ZHANG Weidong  ZHU Jingyi  WU Yejun  YANG Fan  XIAO Liang 

Cite this article as: Xie JP, Zhang WD, Zhu JY, et al. The clinical value of T1 and T2 values in predicting brain glioma grading and cell proliferation activity[J]. Chin J Magn Reson Imaging, 2021, 12(1): 15-20. DOI:10.12015/issn.1674-8034.2021.01.004.


[Abstract] Objective To investigate the diagnostic value of T1, T2 and enhanced T1 value in predicting glioma grading and cell proliferation activity.Materials and Methods Thirty-six patients with glioma confirmed by surgery and pathology were retrospectively analyzed, including 21 patients with high grade glioma (HGG) and 15 patients with low grade glioma (LGG). All patients underwent Magic scan and Magic contrast-enhanced scan one week before surgery. T1 mapping and T2 mapping were generated before and after enhancement. T1 value before enhancement (T1-pre), T2 value before enhancement (T2-pre) and T1 value after enhancement (T1-Gd) were measured in the solid portion of the tumor and normal white matter in the contralateral mirror. The pathological grades and Ki-67 labeling index (Ki-67 LI) of the surgical specimens were measured. The correlation between Magic parameters and Ki-67 LI and the difference between HGG and LGG of Magic parameters and Ki-67 LI were analyzed, and the ROC curve was drawn.Result T1-pre, T1 ratio before enhancement (ratio of T1-pre,rT1-pre), T1-Gd, T1 difference before and after enhancement (ΔT1), T1 value percentage change was significantly associated with Ki-67 LI (P<0.05).The correlation coefficients r were 0.502, 0.331, -0.351, 0.537 and 0.473, respectively. T1-pre, ΔT1、T1 value percentage change, Ki-67 LI in HGG was significantly higher than LGG and T1-Gd, ratio of T1-Gd after enhancement (rT1-Gd) in HGG was lower than LGG. The differences were statistically significant (P<0.05). The diagnosis efficiency for ΔT1 to distinguish HGG and LGG was best, the diagnostic threshold was 373.25 ms, area under the curve was 0.816, the sensitivity was 90.5%, and the specificity was 60%, P=0.001.Conclusions Quantitative measurement of T1 value can be used to distinguish high grade glioma from low grade glioma, which has clinical value in predicting tumor cell proliferation.
[Keywords] brain glioma;grading;ki-67 labeling index;magnetic resonance imaging;T1, T2 value

XIE Jiapei1   ZHANG Weidong2   ZHU Jingyi2   WU Yejun1   YANG Fan1   XIAO Liang1*  

1 The Fourth Affiliated Hospital of China Medical University, Shenyang 110000, China

2 The First Affiliated Hospital of China Medical University, Shenyang 110000, China

*Corresponding author: Xiao L, E-mail: xiaoliang_cmu@163com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by the National Natural Science Found of China No. 81471763
Received  2020-09-18
Accepted  2020-11-20
DOI: 10.12015/issn.1674-8034.2021.01.004
Cite this article as: Xie JP, Zhang WD, Zhu JY, et al. The clinical value of T1 and T2 values in predicting brain glioma grading and cell proliferation activity[J]. Chin J Magn Reson Imaging, 2021, 12(1): 15-20. DOI:10.12015/issn.1674-8034.2021.01.004.

1
Weller M, van den Bent M, Hopkins K, et al. EANO guideline for the diagnosis and treatment of anaplastic gliomas and glioblastoma. Lancet Oncol, 2014, 15(9): 395-403. DOI: 10.1016/s1470-2045(14)70011-7
2
Weller M. Novel diagnostic and therapeutic approaches to malignant glioma. Swiss Med Wkly, 2011, 141(w13210): 1-7. DOI: 10.4414/smw.2011.13210
3
Richards-Taylor S, Ewings SM, Jaynes E, et al. The assessment of Ki-67 as a prognostic marker in neuroendocrine tumours: a systematic review and meta-analysis. J Clin Pathol, 2016, 69(7): 612-618. DOI: 10.1136/jclinpath-2015-203340
4
Habberstad AH, Gulati S, Torp SH, et al. Evaluation of the proliferation markers Ki-67/MIB-1, mitosin, survivin, pHH3, and DNA topoisomerase IIalpha in human anaplastic astrocytomas: an immunohistochemical study. Diagn Pathol, 2011, 6(43): 1-8. DOI: 10.1186/1746-1596-6-43
5
Donato V, Papaleo A, Castrichino A, et al. Prognostic implication of clinical and pathologic features in patients with glioblastoma multiforme treated with concomitant radiation plus temozolomide. Tumori, 2007, 93(3): 248-256. DOI: 10.1177/030089160709300304
6
Duchaussoy T, Budzik JF, Norberciak L, et al. Synthetic T2 mapping is correlated with time from stroke onset: a future tool in wake-up stroke management. Eur Radiol, 2019, 29(12): 7019-7026. DOI: 10.1007/s00330-019-06270-0
7
Tanenbaum LN, Tsiouris AJ, Johnson AN, et al. Synthetic MRI for clinical neuroimaging: results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial. AJNR Am J Neuroradiol, 2017, 38(6): 1103-1110. DOI: 10.3174/ajnr.A5227
8
Louis DN, Ohgaki H, Wiestler OD, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol, 2007, 114: 97-109. DOI: DOI10.1007/s00401-007-0243-4
9
Law M, Yang S, Wang H, et al.Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol, 2003, 24(10): 1989-1998. DOI: 10.1016/S0304-3940(03)00219-2
10
Bai Y, Lin YS, Tian J, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology, 2015, 278(2): 496-504. DOI: 10.1148/radiol.2015142173
11
Zhang J, Chen XW, Chen D, et al. Grading and proliferation assessment of diffuse astrocytic tumors with monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging and diffusion kurtosis imaging. Eur J Radiol, 2018, 109(2018): 188-195. DOI: 10.1016/j.ejrad.2018.11.003
12
Fudaba H, Shimomura T, Abe T, et al. Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. AJNR Am J Neuroradiol, 2014, 35(11): 2091-2098. DOI: 10.3174/ajnr.A4018
13
Jain KK, Sahoo P, Tyagi R, et al. Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI. Clin Radiol, 2015, 70(10): 1128-1135. DOI: 10.1016/j.crad.2015.06.076
14
Suh CH, Park JE, Jung SC, et al. Amide proton transfer-weighted MRI in distinguishing high- and low-grade gliomas: a systematic review and meta-analysis. Neuroradiology, 2019, 61(5): 525-534. DOI: 10.1007/s00234-018-02152-2
15
Hansen RK, Bissell MJ. Tissue architecture and breast cancer: the role of extracellular matrix and steroid hormones. Endocr Relat Cancer, 2000, 7(2): 95-113. DOI: 10.1677/erc.0.0070095
16
Hattingen E, Müller A, Jurcoane A, et al. Value of quantitative magnetic resonance imaging T1relaxometry in predicting contrast-enhancement in glioblastoma patients. Oncotarget, 2017, 8: 53542-53551. DOI: 10.18632/oncotarget.18612
17
Wang JN,Zhu JY,Zhang B,et al. The study of T1 and T2 values in differentiating of benign and malignant gliomas. Chin J Magn Reson Imaging, 2020, 11(6): 416-421. DOI: 10.12015/issn.1674-8034.2020.06.004
18
Hattingen E, Jurcoane A, Daneshvar K, et al. Quantitative T2 mapping of recurrent glioblastoma under bevacizumab improves monitoring for non-enhancing tumor progression and predicts overall survival. Neuro Oncol, 2013, 15(10): 1395-404. DOI: 10.1093/neuonc/not105
19
Kern M, Auer TA, Picht T, et al. T2 mapping of molecular subtypes of WHO grade II/III gliomas. BMC Neurol, 2020, 20(1): 1-9. DOI: 10.1186/s12883-019-1590-1
20
Bai Y, Lin YS, Zhang W, et al. Noninvasive amide proton transfer magnetic resonance imaging in evaluating the grading and cellularity of gliomas. Oncotarget, 2017, 84(4): 1-9. DOI: 10.18632/oncotarget.13970
21
Su C, Liu C, Zhao L, et al. Amide proton transfer imaging allows detection of glioma grades and tumor proliferation: comparison with Ki-67 expression and proton MR spectroscopy imaging. AJNR Am J Neuroradiol, 2017, 38(9): 1702-1709. DOI: 10.3174/ajnr.A5301
22
Lescher S, Jurcoane A, Veit A, et al. Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab: earlier detection of tumor progression compared to conventional MRI. Neuroradiology, 2015. 57(1): 11-20. DOI: 10.1007/s00234-014-1445-9
23
Olsen G, Lyng H, Tufto I, et al. Measurement of proliferation activity in human melanoma xenografts by magnetic resonance imaging. Magn Reson Imaging, 1999. 17(3): 393-402. DOI: 10.1016/s0730-725x(98)00175-1
24
Khoury T, Zirpoli G, Cohen SM, et al. Ki-67 Expression in breast cancer tissue microarrays: assessing tumor heterogeneity, concordance with full section, and scoring methods. Am J Clin Pathol, 2017, 148(2): 108-118. DOI: 10.1093/ajcp/aqx053
25
Holli-Helenius K, Salminen A, Rinta-Kiikka I, et al. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study. BMC Med Imaging, 2017, 17(1): 1-9. DOI: 10.1186/s12880-017-0239-z
26
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 Neurooncol, 2019, 141(1): 195-203. DOI: 10.1007/s11060-018-03025-7
27
Matsuda M, Kido T, Tsuda T, et al. Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study. Clin Radiol, 2020, 75(5): 1-8. DOI: 10.1016/j.crad.2019.12.021

PREV Predictors of intracranial hemorrhage after mechanical thrombectomy in acute ischemic stroke
NEXT Gender difference of gray and white matter surface area in major depressive disorder
  



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