Share:
Share this content in WeChat
X
Clinical Article
The value of apparent diffusion coefficient value in evaluating the IDH-1 mutation status and tumor cell proliferation activity of lower-grade gliomas
LIU Xianwang  KE Xiaoai  ZHOU Qing  LI Shenglin  DENG Juan  XUE Caiqiang  HUANG Xiaoyu  SUN Qiu  ZHOU Junlin 

Cite this article as: Liu XW, Ke XA, Zhou Q, et al. The value of apparent diffusion coefficient value in evaluating the IDH-1 mutation status and tumor cell proliferation activity of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2022, 13(8): 13-18. DOI:10.12015/issn.1674-8034.2022.08.003.


[Abstract] Objective To investigate the evaluation value of apparent diffusion coefficient (ADC) value in lower-grade gliomas (LGG) isocitrate dehydrogenase 1 (IDH-1) mutation status and tumor cell proliferation activity.Materials and Methods Forty-four patient cases with LGG were confirmed by pathology, and measured IDH-1 mutation status and the Ki-67 proliferation index was retrospectively analyzed, including 24 cases of IDH-1 mutant-type and 20 cases of IDH-1 wild-type. The minimum ADC value (ADCmin), mean ADC value (ADCmean) of the lesion parenchyma, and the ADC value of the contralateral mirror normal white matter on the ADC maps were measured, and the relative minimum ADC value (rADCmin) and relative mean ADC value (rADCmean) were calculated. The differences in ADC values between the two groups were compared, and receiver operating characteristic (ROC) curves were drawn to evaluate the differential diagnostic efficacy. The Ki-67 proliferation index of the solid tumor components was also measured to explore its relationship with ADC values.Results The ADCmin, ADCmean, rADCmin, and rADCmean values in the IDH-1 mutant-type group were higher than those in the IDH-1 wild-type group, and the differences between the groups were statistically significant (all P<0.05). ROC results show that all parameters can effectively distinguish IDH-1 mutant-type and IDH-1 wild-type LGG. Among them, rADCmin has the best discrimination efficiency, and 0.978 is the best cut-off value, with area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value was 0.838, 80.00%, 83.33%, 81.82%, 80.00%, and 83.30%, respectively. ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 proliferation index showed different degrees of negative correlation (r=-0.552, -0.590, -0.532, -0.579, all P<0.05).Conclusions ADC values can be used to evaluate LGG IDH-1 mutation status, and it also has a certain value for evaluating tumor cell proliferation activity.
[Keywords] brain gliomas;lower-grade gliomas;isocitrate dehydrogenase;Ki-67 proliferation index;magnetic resonance imaging;apparent diffusion coefficient

LIU Xianwang   KE Xiaoai   ZHOU Qing   LI Shenglin   DENG Juan   XUE Caiqiang   HUANG Xiaoyu   SUN Qiu   ZHOU Junlin*  

Department of Radiology, Lanzhou University Second Hospital; Second Clinical School, Lanzhou University; Key Laboratory of Medical Imaging of Gansu Province; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China

Zhou JL, E-mail: LZUzjl601@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81772006, 82071872); Natural Science Foundation of Gansu Province (No. 21JR11RA105); Open Fund of Gansu Provincial Key Laboratory of Medical Imaging (No. GSYX202007).
Received  2021-11-14
Accepted  2022-07-27
DOI: 10.12015/issn.1674-8034.2022.08.003
Cite this article as: Liu XW, Ke XA, Zhou Q, et al. The value of apparent diffusion coefficient value in evaluating the IDH-1 mutation status and tumor cell proliferation activity of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2022, 13(8): 13-18. DOI:10.12015/issn.1674-8034.2022.08.003.

[1]
Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016[J]. Neuro Oncol, 2019, 21(Suppl 5): v1-v100. DOI: 10.1093/neuonc/noz150.
[2]
Chen J, Qian X, He Y, et al. An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients[J/OL]. Brief Bioinform, 2021, 22(6) [2021-11-14]. https://academic.oup.com/bib/article/22/6/bbab190/6278605. DOI: 10.1093/bib/bbab190.
[3]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary[J]. Acta Neuropathol, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1.
[4]
Fukuya Y, Ikuta S, Maruyama T, et al. Tumor recurrence patterns after surgical resection of intracranial low-grade gliomas[J]. J Neurooncol, 2019, 144(3): 519-528. DOI: 10.1007/s11060-019-03250-8.
[5]
Eckel-Passow JE, Lachance DH, Molinaro AM, et al. Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors[J]. N Engl J Med, 2015, 372(26): 2499-2508. DOI: 10.1056/NEJMoa1407279.
[6]
Xu S, Tang L, Dai G, et al. Immune-related genes with APA in microenvironment indicate risk stratification and clinical prognosis in grade Ⅱ/Ⅲ gliomas[J]. Mol Ther Nucleic Acids, 2021, 23: 1229-1242. DOI: 10.1016/j.omtn.2021.01.033.
[7]
Han S, Liu Y, Cai SJ, et al. IDH mutation in glioma: molecular mechanisms and potential therapeutic targets[J]. Br J Cancer, 2020, 122(11): 1580-1589. DOI: 10.1038/s41416-020-0814-x.
[8]
Li Y, Lin CY, Qi YF, et al. Three-dimensional turbo-spin-echo amide proton transfer-weighted and intravoxel incoherent motion MR imaging for type I endometrial carcinoma: Correlation with Ki-67 proliferation status[J]. Magn Reson Imaging, 2021, 78: 18-24. DOI: 10.1016/j.mri.2021.02.006.
[9]
Liu XW, Han L, Liu H, et al. Apparent Diffusion Coefficient to Evaluate Adult Intracranial Ependymomas: Relationship to Ki-67 Proliferation Index[J]. J Neuroimaging, 2021, 31(1): 132-136. DOI: 10.1111/jon.12789.
[10]
Ke XA, Zhou Q, Han L, et al. Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging[J]. Neuroradiology, 2020, 62(5): 601-607. DOI: 10.1007/s00234-020-02374-3.
[11]
Liu ZL, Wang XC, Zhang H, et al. MR diffusion imaging: research advances in prognosis prediction of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(1): 77-80. DOI: 10.12015/issn.1674-8034.2021.01.017.
[12]
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.
[13]
Henker C, Kriesen T, Schneider B, et al. Correlation of Ki-67 Index with Volumetric Segmentation and its Value as a Prognostic Marker in Glioblastoma[J/OL]. World Neurosurg, 2019, 125 [2021-11-14]. https://www.sciencedirect.com/science/article/pii/S1878875019303699?via%3Dihub. DOI: 10.1016/j.wneu.2019.02.006.
[14]
Yan XT, Song SS, Lu J. Research progress of magnetic resonance diffusion weighted imaging in glioma[J]. Journal of Medical Imaging, 2021, 31(9): 1586-1589. DOI: 1006-9011(2021)09-1586-04.
[15]
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.
[16]
Li S, Zhou Q, Zhang P, et al. The relationship between the apparent diffusion coefficient and the Ki-67 proliferation index in intracranial solitary fibrous tumor/hemangiopericytoma[J]. Neurosurg Rev, 2022, 45(2): 1625-1633. DOI: 10.1007/s10143-021-01687-y.
[17]
Xue C, Liu S, Deng J, et al. Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma[J]. Clin Neuroradiol, 2022, 32(1): 269-276. DOI: 10.1007/s00062-021-01134-x.
[18]
Liu X, Deng J, Sun Q, et al. Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis[J]. Neurosurg Rev, 2022, 45(3): 2449-2456. DOI: 10.1007/s10143-022-01771-x.
[19]
Gihr GA, Horvath-Rizea D, Hekeler E, et al. Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology[J/OL]. Front Oncol, 2020, 10 [2021-11-14]. https://www.frontiersin.org/articles/10.3389/fonc.2020.00206/full. DOI: 10.3389/fonc.2020.00206.
[20]
Jiang J, Zhang XL, Zhou JL. Research progress of isocitrate dehydrogenase genotype and imaging in glioma[J]. Chin J Magn Reson Imagin, 2021, 12(5): 103-106. DOI: 10.12015/issn.1674-8034.2021.05.025.
[21]
Darvishi P, Batchala PP, Patrie JT, et al. Prognostic Value of Preoperative MRI Metrics for Diffuse Lower-Grade Glioma Molecular Subtypes[J]. AJNR Am J Neuroradiol, 2020, 41(5): 815-821. DOI: 10.3174/ajnr.A6511.
[22]
Deng KJ, Zheng QY, Fan SP, et al. Prediction of IDH1 gene mutation in glioma by ADC three-dimensional histogram analysis[J]. J Clin Radiol, 2021, 40(5): 864-869. DOI: 10.13437/j.cnki.jcr.2021.05.007.
[23]
Ke XA, Zhang QY, Zhou Q, et al. Magnetic resonance imaging assessment of isocitrate dehydrogenase 1 mutation status in degenerative astrocytoma[J]. Chin J Magn Reson Imaging, 2019, 10(7): 504-508. DOI: 10.12015/issn.1674-8034.2019.07.005.
[24]
Villanueva-Meyer JE, Wood MD, Choi BS, et al. MRI Features and IDH Mutational Status of Grade Ⅱ Diffuse Gliomas: Impact on Diagnosis and Prognosis[J]. AJR Am J Roentgenol, 2018, 210(3): 621-628. DOI: 10.2214/AJR.17.18457.
[25]
Tan WL, Huang WY, Yin B, et al. Can diffusion tensor imaging noninvasively detect IDH1 gene mutations in astrogliomas? A retrospective study of 112 cases[J]. AJNR Am J Neuroradiol, 2014, 35(5): 920-927. DOI: 10.3174/ajnr.A3803.
[26]
Walts AE, Mirocha JM, Marchevsky AM. Challenges in Ki-67assessments in pulmonary large-cell neuroendocrine carcinomas[J]. Histopathology, 2021, 78(5): 699-709. DOI: 10.1111/his.14277.
[27]
Lee JH, Yoon YC, Seo SW, et al. Soft tissue sarcoma: DWI and DCE-MRI parameters correlate with Ki-67 labeling index[J]. Eur Radiol, 2020, 30(2): 914-924. DOI: 10.1007/s00330-019-06445-9.
[28]
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]. J Clin Pathol, 2016, 69(7): 612-618. DOI: 10.1136/jclinpath-2015-203340.
[29]
Wang Y, Bai G, Zhang X, et al. Correlation analysis of apparent diffusion coefficient value and P53 and Ki-67 expression in esophageal squamous cell carcinoma[J]. Magn Reson Imaging, 2020, 68: 183-189. DOI: 10.1016/j.mri.2020.01.011.
[30]
Qian JF, Zhang R, Zhao L, et al. Study on the correlation between MRI diffusion-weighted imaging apparent diffusion coefficient and Ki-67 index of triple negative and non-triple negative breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(5): 69-72. DOI: 10.12015/issn.1674-8034.2021.05.015.
[31]
Zeng Q, Dong F, Shi F, et al. Apparent diffusion coefficient maps obtained from high b value diffusion-weighted imaging in the preoperative evaluation of gliomas at 3T: comparison with standard b value diffusion-weighted imaging[J]. Eur Radiol, 2017, 27(12): 5309-5315. DOI: 10.1007/s00330-017-4910-0.
[32]
Liu XW, Han L, Hao Y, et al. The value of the minimum apparent diffusion coefficient in evaluating the Ki-67 proliferation index in adult intracranial ependymoma[J]. Chin J Magn Reson Imagin, 2020, 11(8): 620-624. DOI: 10.12015/issn.1674-8034.2020.08.005.

PREV Construction of nomogram model for predicting prognosis of low-grade gliomas based on diffusion kurtosis imaging histogram
NEXT Preliminary study of synthetic MRI combined with three-dimensional arterial spin labeling imaging in differentiating recurrence and pseudoprogression of glioma
  



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