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Clinical Article
Research of Ki-67 expression in glioma by multimodal multi-parameter magnetic resonance imaging
ZHOU Jianan  ZHU Zhengyang  TIAN Chuanshuai  YANG Huiquan  Chen Sixuan  YE Meiping  ZHANG Xin  ZHANG Bing 

Cite this article as ZHOU J N, ZHU Z Y, TIAN C S, et al. Research of Ki-67 expression in glioma by multimodal multi-parameter magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(5): 34-40. DOI:10.12015/issn.1674-8034.2024.05.007.


[Abstract] Objective To investigate the prediction of Ki-67 expression in glioma by multi-modal magnetic resonance imaging and quantitative and qualitative parameters.Materials and Methods Three hundred and thirty patients with glioma were selected, including 201 patients with isocitrate dehydrogenase (IDH) wild type and 129 patients with IDH mutant type. Conventional MRI features, apparent diffusion coefficient (ADC), qualitative and quantitative parameters of dynamic contrast-enhanced (DCE) MRI such as time-intensity curves (TIC), volume transfer constant (Ktrans), the rate constant (Kep), fractional volume of the extravascular-extracellular (Ve), plasma fraction (Vp) and magnetic resonance spectroscopy (MRS) metabolites were obtained. Logistic regression was performed for all gliomas to identify factors associated with Ki-67 expression. The area under receiver operating characteristic curve (AUC) was used to evaluate the performance of the prediction model.Results In glioma patients'analyses, Ktrans (OR=1.012, P<0.001), ADC (OR=0.998, P<0.05), enhancement degree (OR=3.317, P<0.05) were independent predictors of Ki-67 expression level, and AUC value is 0.893, respectively.Conclusions Ktrans, ADC and enhancement may be effective parameters for predicting Ki-67 expression level in glioma.
[Keywords] glioma;magnetic resonance imaging;multi-modal;Ki-67;isocitrate dehydrogenase

ZHOU Jianan1, 2   ZHU Zhengyang2   TIAN Chuanshuai1, 2   YANG Huiquan2   Chen Sixuan2   YE Meiping2   ZHANG Xin1, 2*   ZHANG Bing1, 2  

1 Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China

2 Department of Radiology, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China

Corresponding author: ZHANG X, E-mail: zhangxin@njglyy.com

Conflicts of interest   None.

Received  2024-01-02
Accepted  2024-04-30
DOI: 10.12015/issn.1674-8034.2024.05.007
Cite this article as ZHOU J N, ZHU Z Y, TIAN C S, et al. Research of Ki-67 expression in glioma by multimodal multi-parameter magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(5): 34-40. DOI:10.12015/issn.1674-8034.2024.05.007.

[1]
OSTROM QT, GITTLEMAN H, TRUITT G, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011-2015[J]. Neuro Oncol, 2018, 20(suppl_4): iv1-iv86. DOI: 10.1093/neuonc/noy131.
[2]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[3]
BURGER P C, SHIBATA T, KLEIHUES P. The use of the monoclonal antibody Ki-67 in the identification of proliferating cells: application to surgical neuropathology[J]. Am J Surg Pathol, 1986, 10(9): 611-617. DOI: 10.1097/00000478-198609000-00003.
[4]
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: e1093-e1103 [2024-01-02]. https://pubmed.ncbi.nlm.nih.gov/30790732/. DOI: 10.1016/j.wneu.2019.02.006.
[5]
HEYE A K, THRIPPLETON M J, ARMITAGE P A, et al. Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability[J]. NeuroImage, 2016, 125: 446-455. DOI: 10.1016/j.neuroimage.2015.10.018.
[6]
HEYE A K, CULLING R D, VALDÉS HERNÁNDEZ M D C, et al. Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review[J]. NeuroImage Clin, 2014, 6: 262-274. DOI: 10.1016/j.nicl.2014.09.002.
[7]
XING Z, HUANG W, SU Y, et al. Non-invasive prediction of p53 and Ki-67 labelling indices and O-6-methylguanine-DNA methyltransferase promoter methylation status in adult patients with isocitrate dehydrogenase wild-type glioblastomas using diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging combined with conventional MRI[J/OL]. Clin Radiol, 2022, 77(8): e576-e584 [2024-01-02]. https://pubmed.ncbi.nlm.nih.gov/35469666/. DOI: 10.1016/j.crad.2022.03.015.
[8]
DU N, SHU W, LI K, et al. An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma[J/OL]. J Transl Med, 2023, 21(1): 119 [2024-01-02]. https://pubmed.ncbi.nlm.nih.gov/36774480. DOI: 10.1186/s12967-023-03950-w.
[9]
JIANG J S, HUA Y, ZHOU X J, et al. Quantitative assessment of tumor cell proliferation in brain gliomas with dynamic contrast-enhanced MRI[J]. Acad Radiol, 2019, 26(9): 1215-1221. DOI: 10.1016/j.acra.2018.10.012.
[10]
FABIJAŃSKA A. A novel approach for quantification of time-intensity curves in a DCE-MRI image series with an application to prostate cancer[J]. Comput Biol Med, 2016, 73: 119-130. DOI: 10.1016/j.compbiomed.2016.04.010.
[11]
LI X B, SONG Y K, ZHU X L, et al. Diagnostic efficacy of amide proton transfer MRI in the grading of gliomas and predicting tumor cell proliferation[J]. Radiol Pract, 2017, 32(4): 355-359. DOI: 10.13609/j.cnki.1000-0313.2017.04.013.
[12]
ZHANG C, GAO Y. Research progress of magnetic resonance diffusion imaging in glioma grading and IDH genotype prediction[J]. Chin J Magn Reson Imaging, 2023, 14(7): 149-154. DOI: 10.12015/issn.1674-8034.2023.07.027.
[13]
QIN Q, GAO X, ZHANG L, et al. A study on prediction of IDH mutation status and WHO grading of glioma based on 3D amide proton transfer-weighted imaging radiomics model[J]. Chin J New Clin Med, 2023, 16(4): 336-341. DOI: 10.3969/j.issn.1674-3806.2023.04.06.
[14]
ZENG Y Q, XIE W, LI Z F, et al. The value of 3D-PCASL imaging technique in grading and IDH mutation status of diffuse glioma[J]. J Pract Radiol, 2022, 38(5): 693-697. DOI: 10.3969/j.issn.1002-1671.2022.05.001.
[15]
QAMAR S R, JALAL S, NICOLAOU S, et al. Comparison of cardiac computed tomography angiography and transoesophageal echocardiography for device surveillance after left atrial appendage closure[J]. EuroIntervention, 2019, 15(8): 663-670. DOI: 10.4244/EIJ-D-18-01107.
[16]
CLAUSER P, KRUG B, BICKEL H, et al. Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy[J]. Clin Cancer Res, 2021, 27(7): 1941-1948. DOI: 10.1158/1078-0432.CCR-20-3037.
[17]
GAN Z N, MA M X, YAN X J. Relationship between Ki-67 labeling index and grade of glioma evaluated by MR diffusion weighted imaging parameters[J]. J Pract Radiol, 2022, 38(10): 1578-1581, 1612. DOI: 10.3969/j.issn.1002-1671.2022.10.004.
[18]
CHANG T J, SHEN H C, YU M M, 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.
[19]
MENG L, ZHAO X, GUO J, et al. Improved differential diagnosis based on BI-RADS descriptors and apparent diffusion coefficient for breast lesions: A multiparametric MRI analysis as compared to kaiser score[J]. Acad Radiol, 2023, 30(Suppl 2): S93-S103. DOI: 10.1016/j.acra.2023.03.035.
[20]
HIRSCHLER L, SOLLMANN N, SCHMITZ-ABECASSIS B, et al. Advanced MR techniques for preoperative glioma characterization: Part 1[J]. J Magn Reson Imaging, 2023, 57(6): 1655-1675. DOI: 10.1002/jmri.28662.
[21]
GUIDA L, STUMPO V, BELLOMO J, et al. Hemodynamic imaging in cerebral diffuse glioma-part A: Concept, differential diagnosis and tumor grading[J/OL]. Cancers (Basel), 2022, 14(6): 1432 [2024-01-02]. https://pubmed.ncbi.nlm.nih.gov/35326580/. DOI: 10.3390/cancers14061432.
[22]
LIU Z Q, LIU H S. Research progress of dynamic contrast-enhanced MRI in diagnosis of glioma[J]. Chin J Med Imaging Technol, 2016, 32(10): 1617-1620. DOI: 10.13929/j.1003-3289.2016.10.036.
[23]
LI S H, SHEN N X, WU D, et al. A comparative study between tumor blood vessels and dynamic contrast-enhanced MRI for identifying isocitrate dehydrogenase gene 1 (IDH1) mutation status in glioma[J]. Curr Med Sci, 2022, 42(3): 650-657. DOI: 10.1007/s11596-022-2563-y.
[24]
WANG N, YIN H, KANG X W, et al. Correlation of Ki-67 labeling index with quantitative dynamic contrast-enhanced MRI in glioma[J]. Radiol Pract, 2019, 34(4): 417-421. DOI: 10.13609/j.cnki.1000-0313.2019.04.010.
[25]
LI Z, ZHAO W, HE B, et al. Application of distributed parameter model to assessment of glioma IDH mutation status by dynamic contrast-enhanced magnetic resonance imaging[J/OL]. Contrast Media Mol Imaging, 2020, 2020: 8843084 [2024-01-02]. https://pubmed.ncbi.nlm.nih.gov/33299387/. DOI: 10.1155/2020/8843084.
[26]
LIU Z, YAO B, WEN J, et al. Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions[J]. Eur Radiol, 2024, 34(1): 182-192. DOI: 10.1007/s00330-023-10102-7.
[27]
ZARIC O, HATAMIKIA S, GEORGE G, et al. AI-based time-intensity-curve assessment of breast tumors on MRI[J]. Eur Radiol, 2024, 34(1): 179-181. DOI: 10.1007/s00330-023-10298-8.
[28]
LI J Y, ZHAO D J. Advances in imaging diagnosis of breast MRI non-lumpy enhancement lesions[J]. Chin J Med Imaging, 2018, 26(7): 547-551. DOI: 10.3969/j.issn.1005-5185.2018.07.019.
[29]
XU L, WANG L, XU D Z, et al. Multimodality MRI in diagnosis of breast lesions with ring enhancement[J]. Chin J Med Imaging Technol, 2017, 33(11): 1661-1665. DOI: 10.13929/j.1003-3289.201702006.
[30]
YANG J, SU D K, ZHAO X, et al. The diagnostic value of combining dynamic contrast enhanced MRI and diffusion weighted imaging in breast lesions with peripheral enhancement[J]. J Clin Radiol, 2016, 35(10): 1490-1494. DOI: 10.13437/j.cnki.jcr.2016.10.011.
[31]
YE M P, YANG B, ZHU Z Y, et al. Prediction of glioma Ki-67 expression grading bas-ed on MRI features[J]. J Clin Radiol, 2023, 42(1): 6-13. DOI: 10.13437/j.cnki.jcr.2023.01.037.
[32]
FAN J K, CHENG Y, HUANG H, et al. Prediction of Ki-67 index expression level in WHO Grade Ⅱ -Ⅲ gliomas by radiomics based on T2-fluid attenuated inversion recovery[J]. Chin J Med Imaging, 2023, 31(4): 315-320. DOI: 10.3969/j.issn.1005-5185.2023.04.003.
[33]
SUN Y, LI H, WU Y, et al. Correlations of mutations of IDH1, IDH2, p53 gene and Ki-67 protein expression in gliomas with the clini cal features of pathological gr-ading[J]. J Pract Med, 2018, 34(9): 1455-1459. DOI: 10.3969/j.issn.1006-5725.2018.09.012.
[34]
CHEN Y H, CHEN D J, CHEN M, et al. Application of DSC-PWI in Grading and Ki-67 Expression of Glioma[J]. Chin J CT & MRI, 2023, 21(2): 4-6. DOI: 10.3969/j.issn.1672-5131.2023.02.003.
[35]
BATALOV A I, ZAKHAROVA N E, CHEKHONIN I V, et al. Arterial spin labeling perfusion in determining the IDH1 status and Ki-67 index in brain gliomas[J]. Diagnostics (Basel), 2022, 12(6): 1444 [2024-01-02]. https://pubmed.ncbi.nlm.nih.gov/35741254/. DOI: 10.3390/diagnostics12061444.

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