Share：
Share this content in WeChat
X
[Chinese][PDF]75121
• Clinical Article •
Value of MRI histogram in the differential diagnosis of dysembryoplastic neuroepithelial tumor and diffuse astrocytoma
ZHAO Wei  DING Shuang  HANJIAERBIEKE·Kukun   WANG Baolong  WANG Yunling 

Cite this article as: Zhao W, Ding S, Hanjiaerbieke·K , et al. Value of MRI histogram in the differential diagnosis of dysembryoplastic neuroepithelial tumor and diffuse astrocytoma[J]. Chin J Magn Reson Imaging, 2022, 13(7): 17-21,54. DOI:10.12015/issn.1674-8034.2022.07.004.


[Abstract] Objective To explore the value of the MRI histogram analysis in differential diagnosis of dysembryoplastic neuroepithelial tumor (DNET) and diffuse astrocytoma (DA).Materials and Methods The general clinical data and imaging findings of 21 patients with DNET and 35 patients with DA who underwent surgery and were confirmed by pathological biopsy in the Department of Neurosurgery of the First Affiliated Hospital of Xinjiang Medical University from December 2014 to December 2021 were retrospectively analyzed. The conventional imaging features of the two groups were first analyzed, and then the tumors in their preoperative MRI T2 fluid attenuated inversion recovery axial images were outlined and subjected to histogram analysis, and histogram parameters such as mean, median, standard deviation, heterogeneity, kurtosis, skewness and entropy of the tumors were extracted, and the histogram parameters of DNET and DA were compared and statistically analyzed to observe and compare the function of each parameter for disease diagnosis.Results General information such as age, gender and tumor site of DNET and DA patients were compared, and the differences were not statistically significant (P＞0.05). The inverted triangle sign imaging sign was statistically significant for differential diagnosis between the two groups of patients (P＜0.05). The difference between the mean, median and kurtosis of DNET and DA was found to be statistically significant (P＜0.05), with kurtosis having the greatest univariate differential diagnostic value, with an area under the curve (AUC) value of 0.690 for the receiver operating characteristic curve and sensitivity and specificity of 68.6% and 66.7%, respectively. The AUC of mean combined with kurtosis was the highest, and the AUC, sensitivity and specificity were 0.721, 66.7% and 77.1%, respectively. Therefore, the differential diagnostic efficacy of mean combined with kurtosis was higher than that of individual histogram analysis parameters. The differential diagnostic efficacy of combining the inverse triangle sign with the histogram analysis parameters was significantly improved, and the mean, median, and kurtosis combined with the inverse triangle sign had the best differential diagnostic efficacy, with an AUC value of 0.830, sensitivity, specificity, and accuracy of 85.7%, 74.3%, and 78.6%, respectively.Conclusions For DNET and DA, which are difficult to distinguish on preoperative MRI, the histogram analysis technique combined with the inverted triangle sign can provide a more accurate differential diagnosis of the two.
[Keywords] dysembryoplastic neuroepithelial tumor；diffuse astrocytoma；histogram analysis；magnetic resonance imaging；differential diagnosis

ZHAO Wei   DING Shuang   HANJIAERBIEKE·Kukun    WANG Baolong   WANG Yunling*  

Nuclear Magnetic Resonance Room, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China

Wang YL, E-mail: 1079806994@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Xinjiang Uygur Autonomous Region Science and Technology Support Project Program for Xinjiang (Directive) Project (No. 2020E0275).
Received  2022-04-23
Accepted  2022-05-05
DOI: 10.12015/issn.1674-8034.2022.07.004
Cite this article as: Zhao W, Ding S, Hanjiaerbieke·K , et al. Value of MRI histogram in the differential diagnosis of dysembryoplastic neuroepithelial tumor and diffuse astrocytoma[J]. Chin J Magn Reson Imaging, 2022, 13(7): 17-21,54. DOI:10.12015/issn.1674-8034.2022.07.004.

[1]
Daumas-Duport C, Scheithauer BW, Chodkiewicz JP, et al. Dysembryoplastic neuroepithelial tumor: a surgically curable tumor of young patients with intractable partial seizures. Report of thirty-nine cases[J]. Neurosurgery, 1988, 23(5): 545-556. DOI: 10.1227/00006123-198811000-00002.
[2]
Delgado-López PD, Corrales-García EM, Martino J, et al. Diffuse low-grade glioma: a review on the new molecular classification, natural history and current management strategies[J]. Clin Transl Oncol, 2017, 19(8): 931-944. DOI: 10.1007/s12094-017-1631-4.
[3]
Shi Z, Li J, Zhao M, et al. Quantitative histogram analysis on intracranial atherosclerotic plaques: a high-resolution magnetic resonance imaging study[J]. Stroke, 2020, 51(7): 2161-2169. DOI: 10.1161/STROKEAHA.120.029062.
[4]
Yuan FY, Zheng T, Yang H, et al. Dysembryoplastic neuroepithelial tumour in the cerebellum: case report and literature review[J]. Br J Neurosurg, 2021, 35(1): 40-42. DOI: 10.1080/02688697.2020.1749990.
[5]
Luzzi S, Elia A, del Maestro M, et al. Dysembryoplastic neuroepithelial tumors: what You need to know[J]. World Neurosurg, 2019, 127: 255-265. DOI: 10.1016/j.wneu.2019.04.056.
[6]
Onishi S, Amatya VJ, Kolakshyapati M, et al. T2-FLAIR mismatch sign in dysembryoplasticneuroepithelial tumor[J]. Eur J Radiol, 2020, 126: 108924. DOI: 10.1016/j.ejrad.2020.108924.
[7]
Kikuchi K, Togao O, Yamashita K, et al. Diagnostic accuracy for the epileptogenic zone detection in focal epilepsy could be higher in FDG-PET/MRI than in FDG-PET/CT[J]. Eur Radiol, 2021, 31(5): 2915-2922. DOI: 10.1007/s00330-020-07389-1.
[8]
García-Casares N, Alfaro-Rubio F, Ramos-Rodríguez JR, et al. Preoperative evaluation by functional magnetic resonance imaging in patients with dysembryoplastic neuroepithelial tumours: a case series[J]. Neurocirugia (Astur: Engl Ed), 2020, 31(4): 158-164. DOI: 10.1016/j.neucir.2019.09.004.
[9]
Louis DN, 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.
[10]
Gupta P, Siraj F, Malik A, et al. Clinical and histopathological profile of dysembryoplastic neuroepithelial tumor: an experience from a tertiary care center[J]. J Cancer Res Ther, 2021, 17(4): 912-916. DOI: 10.4103/jcrt.JCRT_632_19.
[11]
Sontowska I, Matyja E, Malejczyk J, et al. Dysembryoplastic neuroepithelial tumour: insight into the pathology and pathogenesis[J]. Folia Neuropathol, 2017, 55(1): 1-13. DOI: 10.5114/fn.2017.66708.
[12]
Lu FF, Xiao H, Yu H, et al. Imaging diagnosis and differential diagnosis of dysembry-oplastic neuroepithelial tumor[J]. Chin J CT MRI, 2017, 15(2): 149-152. DOI: 10.3969/j.issn.1672-5131.2017.02.047.
[13]
Yang XC, Chen L. MRI diagnosis and differential diagnosis of dysplastic neuroepithelial tumor (analysis of 24 cases)[J]. Chin J CT MRI, 2021, 19(1): 31-35. DOI: 10.3969/j.issn.1672-5131.2021.01.011.
[14]
Peng C, ZHOU Jian. Research progress on epilepsy associated with dysfetal neuroepithelial tumors[J]. Chin J Minim Invasive Neurosurg, 2021, 26(4): 187-189. DOI: 10.11850/j.issn.1009-122X.2021.04.015.
[15]
Fernandez C, Girard N, Paz Paredes A, et al. The usefulness of MR imaging in the diagnosis of dysembryoplastic neuroepithelial tumor in children: a study of 14 cases[J]. AJNR Am J Neuroradiol, 2003, 24(5): 829-834.
[16]
Takita H, Shimono T, Uda T, et al. Malignant transformation of a dysembryoplastic neuroepithelial tumor presenting with intraventricular hemorrhage[J]. Radiol Case Rep, 2022, 17(3): 939-943. DOI: 10.1016/j.radcr.2022.01.014.
[17]
Mohile NA, Messersmith H, Gatson NT, et al. Therapy for diffuse astrocytic and oligodendroglial tumors in adults: ASCO-SNO guideline[J]. J Clin Oncol, 2022, 40(4): 403-426. DOI: 10.1200/JCO.21.02036.
[18]
Bisdas S, Koh TS, Roder C, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: feasibility of the method and initial results[J]. Neuroradiology, 2013, 55(10): 1189-1196. DOI: 10.1007/s00234-013-1229-7.
[19]
Leung D, Han XS, Mikkelsen T, et al. Role of MRI in primary brain tumor evaluation[J]. J Natl Compr Canc Netw, 2014, 12(11): 1561-1568. DOI: 10.6004/jnccn.2014.0156.
[20]
Béresová M, Larroza A, Arana E, et al. 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution[J]. MAGMA, 2018, 31(2): 285-294. DOI: 10.1007/s10334-017-0653-9.
[21]
Tian ZR, Chen CY, Fan YM, et al. Glioblastoma and anaplastic astrocytoma: differentiation using MRI texture analysis[J]. Front Oncol, 2019, 9: 876. DOI: 10.3389/fonc.2019.00876.
[22]
Su CQ, Lu SS, Han QY, et al. Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading[J]. Acta Radiol, 2019, 60(6): 777-787. DOI: 10.1177/0284185118801127.
[23]
Sacconi B, Anzidei M, Leonardi A, et al. Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates[J]. Clin Radiol, 2017, 72(6): 443-450. DOI: 10.1016/j.crad.2017.01.015.
[24]
Zhang S, Chiang GCY, Magge RS, et al. MRI based texture analysis to classify low grade gliomas into astrocytoma and 1p/19q codeleted oligodendroglioma[J]. Magn Reson Imaging, 2019, 57: 254-258. DOI: 10.1016/j.mri.2018.11.008.

PREV Application of synthetic diffusion-weighted imaging in evaluating the grading of glioma
NEXT Differentiating salivary gland pleomorphic adenoma from basal cell adenoma based on multimodal magnetic resonance imaging radiomics
  


LINK


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