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Application value of MRI texture analysis based on GLCM in differential diagnosis of intraspinal meningioma and schwannoma
LIANG Jie  DU Xin  WANG Xianliang  PU Rujian  ZHU Wanping 

Cite this article as: Liang J, Du X, Wang XL, et al. Application value of MRI texture analysis based on GLCM in differential diagnosis of intraspinal meningioma and schwannoma[J]. Chin J Magn Reson Imaging, 2022, 13(8): 84-87. DOI:10.12015/issn.1674-8034.2022.08.016.


[Abstract] Objective To investigate the clinical value of MRI texture analysis based on gray-level co-occurrence matrix (GLCM) in differentiating intraspinal meningioma and schwannoma.Materials and Methods Thirty-two cases of intraspinal schwannoma and 26 cases of meningioma confirmed by pathology were analyzed retrospectively. The region of interest (ROI) of the largest layer of the tumor was selected in T2WI and contrast-enhanced T1WI sagittal images by using imageJ software, and the GLCM texture parameters of the lesions were extracted.Results The differences of tumor parameters between the two groups were compared, and the diagnostic efficiency of each parameter was evaluated. There was a significant difference between the two groups (P<0.05); there was significant difference in energy, contrast, correlation and entropy between the two groups in contrast-enhanced T1WI sequence (P<0.05). The energy and correlation of schwannoma group were less than that of meningioma group, the contrast and entropy were greater than that of meningioma group, and there was no significant difference between inverse gap groups (P>0.05). ROC curve analysis showed that the entropy in T2WI sequence and the energy diagnosis efficiency in contrast-enhanced T1WI sequence were the best. The joint diagnosis of texture parameters by logistic regression analysis has improved the diagnosis efficiency compared with that of single parameter.Conclusions MRI texture analysis based on GLCM has certain clinical value in the differential diagnosis of intraspinal meningioma and schwannoma.
[Keywords] magnetic resonance imaging;texture analysis;meningioma;schwannoma;gray-level co-occurrence matrix

LIANG Jie1   DU Xin1   WANG Xianliang1   PU Rujian2   ZHU Wanping3*  

1 Department of Radiology, Weifang People's Hospital, Weifang 261041, China

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

3 Department of Spinal Surgery, Weifang People's Hospital, Weifang 261041, China

Zhu WP, E-mail: zwplj2020@126.com

Conflicts of interest   None.

Received  2022-04-20
Accepted  2022-08-05
DOI: 10.12015/issn.1674-8034.2022.08.016
Cite this article as: Liang J, Du X, Wang XL, et al. Application value of MRI texture analysis based on GLCM in differential diagnosis of intraspinal meningioma and schwannoma[J]. Chin J Magn Reson Imaging, 2022, 13(8): 84-87.DOI:10.12015/issn.1674-8034.2022.08.016

[1]
DiGiorgio AM, Virk MS, Mummaneni PV. Spinal meningiomas[J]. Handb Clin Neurol, 2020, 170: 251-256. DOI: 10.1016/B978-0-12-822198-3.00045-8.
[2]
Zhai X, Zhou M, Chen H, et al. Differentiation between intraspinal schwannoma and meningioma by MR characteristics and clinic features[J]. Radiol Med, 2019, 124(6): 510-521. DOI: 10.1007/s11547-019-00988-z.
[3]
Gu R, Liu JB, Zhang Q, et al. MRI diagnosis of intradural extramedullary tumors[J]. J Cancer Res Ther, 2014, 10(4): 927-931. DOI: 10.4103/0973-1482.137993.
[4]
Matsumura Y, Yamaguchi H, Watanabe K, et al. Lateral-or prone-position video-assisted thoracic surgery for dumbbell-type posterior mediastinal tumors: pros and cons[J]. Indian J Thorac Cardiovasc Surg, 2022, 38(4): 430-433. DOI: 10.1007/s12055-022-01343-0.
[5]
Maiti TK, Bir SC, Patra DP, et al. Spinal meningiomas: clinicoradiological factors predicting recurrence and functional outcome[J/OL]. Neurosurg Focus, 2016, 41(2) [2022-04-20]. https://thejns.org/focus/view/journals/neurosurg-focus/41/2/article-pE6.xml. DOI: 10.3171/2016.5.FOCUS16163.
[6]
Gilard V, Goia A, Ferracci FX, et al. Spinal meningioma and factors predictive of post-operative deterioration[J]. J Neurooncol, 2018, 140(1): 49-54. DOI: 10.1007/s11060-018-2929-y.
[7]
Sadrameli SS, Chan TM, Lee JJ, et al. Resection of Spinal Meningioma Using Ultrasonic BoneScalpel Microshaver: Cases, Technique, and Review of the Literature[J]. Oper Neurosurg (Hagerstown), 2020, 19(6): 715-720. DOI: 10.1093/ons/opaa223.
[8]
Xia LL, Tang J, Huang SL. Primary intraspinal benign tumors treated surgically: an analysis from China[J]. Br J Neurosurg, 2021, 35(5): 603-606. DOI: 10.1080/02688697.2021.1923648.
[9]
Lu ZW, Tian X, Sun Q, et al. The difference between spinal meningiomas and schwannoma on MRI appearance[J]. Journal of Medical Imaging, 2012, 22(8): 1250-1253. DOI: 10.3969/j.issn.1006-9011.2012.08.006.
[10]
Xie YH, Fan Y, Zhao L, et al. MRI diagnosis of the case of multiple intra-spinal meningeoma[J]. Chin J Magn Reson Imaging, 2012, 3(6): 478-479 DOI: 10.3969/j.issn.1674-8034.2012.06.014.
[11]
Almeida M, Santos I. Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images[J]. J Imaging, 2020, 6(6): 51. DOI: 10.3390/jimaging6060051.
[12]
Zhang Y, Zhuang Y, Ge Y, et al. MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke[J/OL]. BMC Med Imaging, 22(1) [2022-04-20]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-022-00845-y. DOI: 10.1186/s12880-022-00845-y.
[13]
Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications[J/OL]. Br J Radiol, 2017, 90(1070) [2022-04-20]. https://www.birpublications.org/doi/10.1259/bjr.20160642. DOI: 10.1259/bjr.20160642.
[14]
Chen J, Wang HY, Ye HY. Research progress of texture analysis in tumor imaging[J]. Chin J Radiol, 2017, 51(12): 979-982. DOI: 10.3760/cma.j.issn.1005-1201.2017.12.020.
[15]
Yang G, He Y, Li X, et al. Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O2 Content and NOx[J]. ACS Omega, 2022, 7(5): 3889-3899. DOI: 10.1021/acsomega.1c03397.
[16]
Dong TF, Mai H, Wei HH, et al. Gray level co-occurrence matrix based on T2WI in differential diagnosis of benign and malignant ovarian solid tumors[J]. Chin J Med Imaging Technol, 2018, 34(9): 1377-1380. DOI: 10.13929/j.1003-3289.201802005.
[17]
Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, et al. Three-dimensional solid texture analysis in biomedical imaging: review and opportunities[J]. Med Image Anal, 2014, 18(1): 176-196. DOI: 10.1016/j.media.2013.10.005.
[18]
Dhruv B, Mittal N, Modi M. Study of Haralick's and GLCM texture analysis on 3D medical images[J]. Int J Neurosci, 2019, 129(4): 350-362. DOI: 10.1080/00207454.2018.1536052.
[19]
Fan TW, Malhi H, Varghese B, et al. Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma[J]. Abdom Radiol (NY), 2019, 44(1): 201-208. DOI: 10.1007/s00261-018-1694-x.
[20]
Tan J, Gao Y, Liang Z, et al. 3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography[J]. IEEE Trans Med Imaging, 2020, 39(6): 2013-2024. DOI: 10.1109/TMI.2019.2963177.
[21]
Koeller KK, Shih RY. Intradural Extramedullary Spinal Neoplasms: Radiologic-Pathologic Correlation[J]. Radiographics, 2019, 39(2): 468-490. DOI: 10.1148/rg.2019180200.
[22]
Dhruv B, Mittal N, Modi M. Study of Haralick's and GLCM texture analysis on 3D medical images[J]. Int J Neurosci, 2019, 129(4): 350-362. DOI: 10.1080/00207454.2018.1536052.
[23]
Cetinkal A, Atabey C, Kaya S, et al. Intraosseous schwannoma of thoracic 12 vertebra without spinal canal involvement[J]. Eur Spine J, 2009, 18(Suppl 2): 236-239. DOI: 10.1007/s00586-009-0922-z.
[24]
Takashima H, Takebayashi T, Yoshimoto M, et al. Differentiating spinal intradural-extramedullary schwannoma from meningioma using MRI T2 weighted images[J/OL]. Br J Radiol, 2018, 91(1092) [2022-04-20]. https://www.birpublications.org/doi/10.1259/bjr.20180262. DOI: 10.1259/bjr.20180262.
[25]
Chen L, Zhou YF, Wu C, et al. CT and MRI imaging characteristics of peripheral schwannoma in different location[J]. Chin J Magn Reson Imaging, 2020, 11(2): 145-148. DOI: 10.12015/issn.1674-8034.2020.02.014.
[26]
Nagano H, Sakai K, Tazoe J, et al. Whole-tumor histogram analysis of DWI and QSI for differentiating between meningioma and schwannoma: a pilot study[J]. Jpn J Radiol, 2019, 37(10): 694-700. DOI: 10.1007/s11604-019-00862-y.
[27]
Feng M, Zhang M, Liu Y, et al. Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study[J/OL]. BMC Cancer, 20(1) [2022-04-20]. https://bmccancer.biomedcentral.com/articles/10.1186/s12885-020-07094-8. DOI: 10.1186/s12885-020-07094-8.
[28]
Dong JY, Miao YW, Liu S, et al. Texture analysis of conventional MRI parameters for differentiating between hemangioma meningioma and hemangiopericytoma based on whole tumor measurement[J]. Chin J Magn Reson Imaging, 2018, 9(4): 258-264. DOI: 10.12015/issn.1674-8034.2018.04.004.

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