Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma

Chinese Journal of Academic Radiology - 2020
Huiyu Huang1, Yong Zhang1, Jingliang Cheng1, Mengmeng Wen1
1Magnetic Resonance Imaging (MRI) Division, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China

Tóm tắt

Abstract Objective To study the value of whole-tumor histogram analysis which is based on apparent diffusion coefficient maps in grading diagnosis of ependymoma. Methods 71 patients with ependymal tumors were retrospectively analyzed, including 13 cases of WHO grade I, 28 cases of WHO grade II, and 30 cases of WHO grade III. Mazda software was used to draw the region of interest (ROI) in the apparent diffusion coefficient maps of three groups on every layer of tumor level. The whole-tumor gray histogram analysis was carried to obtained nine characteristic parameters, including mean, variance, kurtosis, skewness, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%. When the parameters satisfy the test of normal distribution and homogeneity of variance, single factor analysis of variance (ANOVA) was carried to compare the three groups and LSD t test was performed to compare the two groups. Besides, the ROC curve was used to analyze the diagnostic efficacy of the parameters. Results Variance, Perc.01%, and Perc.10% had significant differences among the three groups (all P < 0.05). The remaining six parameters had no significant difference among the three groups (all P > 0.05). And, between WHO I and WHO II, the sensitivity and specificity of the Perc.10% were 85.7% and 100.0%, the AUC was 0.872, and the cut-off was 126.5. Between WHO I and WHO III, the sensitivity and specificity of the Perc.10% were 85.7% and 87.7%, the AUC was 0.835, and the optimum critical value was 131.33. Besides, the sensitivity, specificity, and AUC of variance between WHO II and WHO III are 68.4%, 76.9%, 0.794, and 2645.7, respectively. They had higher identification efficiency. Conclusion Whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps could provide ancillary diagnostic value in grading diagnosis of ependymoma. Perc.10% had a high diagnostic efficiency.

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Tài liệu tham khảo

DeWitt JC, Mock A, Louis DN. The 2016 WHO classification of central nervous system tumors: what neurologists need to know[J]. Curr Opin Neurol. 2017;30(6):643–9. https://doi.org/10.1097/wco.0000000000000490.

Kobyakov GL, Absalyamova OV, Poddubskiy AA, et al. The 2016 WHO classification of primary central nervous system tumors: a clinician’s view[J]. Zh Vopr Neirokhir Im N N Burdenko. 2018;82(3):88–96. https://doi.org/10.17116/neiro201882388.

Poretti A, Meoded A, Huisman TA. Neuroimaging of pediatric posterior fossa tumors including review of the literature[J]. J Magn Reson Imaging. 2012;35(1):32–47. https://doi.org/10.1002/jmri.22722(Epub 2011 Oct 11).

Wu CC, Guo WY, Chung WY, et al. Tumor pseudoprogression and true progression following gamma knife radiosurgery for recurrent ependymoma [J]. J Chin Med Assoc. 2016;79(5):292–8. https://doi.org/10.1016/j.jcma.2015.10.005.

Zitouni S, Koc G, Doganay S, et al. Apparent diffusion coefficient in differentiation of pediatric posterior fossa tumors[J]. Jpn J Radiol. 2017;35(8):448–53. https://doi.org/10.1007/s11604-017-0652-9(Epub 2017 May 26).

Kang Y, Choi SH, Kim YJ, et al. Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging–correlation with tumor grade[J]. Radiology. 2011;261(3):882–90. https://doi.org/10.1148/radiol.11110686.

Andersen MB, Harders SW, Ganeshan B, et al. CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer[J]. Acta Radiol. 2016;57(6):669–76. https://doi.org/10.1177/0284185115598808.

Jung SC, Yeom JA, Kim JH, et al. Glioma: application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading[J]. AJNR. 2014;35(6):1103–10. https://doi.org/10.3174/ajnr.A3825.

Andersen MB, Harders SW, Ganeshan B, et al. CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer[J]. Acta Radiol. 2016;57(6):669–76. https://doi.org/10.1177/0284185115598808.

Zhang Z, Song C, Zhang Y, et al. Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging[J]. Dentomaxillofac Radiol. 2019;9:20190100. https://doi.org/10.1259/dmfr.20190100.

Shindo T, Fukukura Y, Umanodan T, et al. Histogram analysis of apparent diffusion coefficient in differentiating pancreatic adenocarcinoma and neuroendocrine tumor[J]. Medicine (Baltimore). 2016;95:e2574. https://doi.org/10.1097/MD.0000000000002574.

Wang W, Cheng J, Zhang Y, et al. Use of apparent diffusion coefficient histogram in differentiating between medulloblastoma and pilocytic astrocytoma in children[J]. Med Sci Monit. 2018;24:6107–12. https://doi.org/10.12659/MSM.909136.

Zulfiqar M, Yousem DM, Lai H. ADC values and prognosis of malignant astrocytomas: does lower ADC predict a worse prognosis independent of grade of tumor?—a meta-analysis[J]. AJR Am J Roentgenol. 2013;200(3):624–9. https://doi.org/10.2214/AJR.12.8679.

Lin X, Lee M, Buck O, et al. Diagnostic accuracy of T1-weighted dynamic contrast-enhanced-MRI and DWI-ADC for differentiation of glioblastoma and primary CNS lymphoma[J]. AJNR Am J Neuroradiol. 2017;38(3):485–91. https://doi.org/10.3174/ajnr.a5023.

Zhang X, Yan LF, Hu YC, et al. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features[J]. Oncotarget. 2017;8(29):47816–30. https://doi.org/10.18632/oncotarget.18001.

Jütten K, Mainz V, Gauggel S, et al. Diffusion tensor imaging reveals microstructural heterogeneity of normal-appearing white matter and related cognitive dysfunction in glioma patients[J]. Front Oncol. 2019;9:536. https://doi.org/10.3389/fonc.2019.00536.eCollection.2019.

Chenevert TL, Malyarenko DI, Galbán CJ, et al. Comparison of voxel-wise and histogram analyses of glioma ADC maps for prediction of early therapeutic change[J]. Tomography. 2019;5(1):7–14. https://doi.org/10.18383/j.tom.2018.00049.

Skogen K, Schulz A, Dormagen JB, et al. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas[J]. Eur J Radiol. 2016;85(4):824–9. https://doi.org/10.1016/j.ejrad.2016.01.013.

Payabvash S, Tihan T, Cha S, et al. Volumetric voxelwise apparent diffusion coefficient histogram analysis for differentiation of the fourth ventricular tumors[J]. Neuroradiol J. 2018;31(6):554–64. https://doi.org/10.1177/1971400918800803(Epub 2018 Sep 19).

Lu SS, Kim SJ, Kim N, et al. Histogram analysis of apparent: diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions [J]. RJRAm J Roentgenol. 2015;204(4):827–34. https://doi.org/10.2214/AJR.14.12677.

Wang Q, Li H, Yan X, et al. Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer[J]. Urol Oncol. 2015;33(8):337.e15–24. https://doi.org/10.1016/j.urolonc.2015.05.005.

Xu XQ, Li Y, Hong XN, et al. Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine angle area: differentiating using whole-tumor histogram analysis of apparent diffusion coefficient[J]. Int J Neurosci. 2017;127(2):183–90. https://doi.org/10.3109/00207454.2016.1164157.

Rodriguez Gutierrez D, Awwad A, Meijer L, et al. Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors[J]. AJNR Am J Neuroradiol. 2014;35(5):1009–15. https://doi.org/10.3174/ajnr.A3784Epub 2013 Dec 5.

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Chinese Journal of Academic Radiology

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