Research Article
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Year 2023, Volume: 9 Issue: 2, 285 - 290, 04.03.2023
https://doi.org/10.18621/eurj.1073259

Abstract

References

  • 1. Kanda T, Miyazaki A, Zeng F, Ueno Y, Sofue K, Maeda T, et al. Magnetic resonance imaging of intraocular optic nerve disorders: review article. Pol J Radiol 2020;85:e67-81.
  • 2. Dooley MC, Foroozan R. Optic neuritis. J Ophthalmic Vis Res 2010;5:182-7.
  • 3. Hoorbakht H, Bagherkashi F. Optic neuritis, its differential diagnosis and management. Open Ophthalmol J 2012;6:65-72.
  • 4. Bennett JL. Optic Neuritis. Continuum (Minneap Minn) 2019;25:1236-64.
  • 5. Petzold A, Wattjes MP, Costello F, Flores-Rivera J, Fraser CL, Fujihara K, et al. The investigation of acute optic neuritis: a review and proposed protocol. Nat Rev Neurol 2014;10:447-58.
  • 6. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol 2004;59:1061-9.
  • 7. Jian ZC, Long JF, Liu YJ, Hu XD, Liu JB, Shi XQ, et al. Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis. World J Clin Cases 2019;7:1122-32.
  • 8. Kwon MR, Shin JH, Hahn SY, Oh YL, Kwak JY, Lee E, et al. Histogram analysis of grey scale sonograms to differentiate between the subtypes of follicular variant of papillary thyroid cancer. Clin Radiol 2018;73:591.e1-591.e7.
  • 9. Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: a review. J Magn Reson Imaging 2019;49:927-38.
  • 10. Cauley KA, Fielden SW. A Radiodensity histogram study of the brain in multiple sclerosis. Tomography 2018;4:194-203.
  • 11. Chabat F, Yang GZ, Hansell DM. Obstructive lung diseases: texture classification for differentiation at CT. Radiology 2003;228:871-7.
  • 12. Dogan A, Baykara M. The evaluation of the optic nerve in multiple sclerosis using MRI histogram analysis. Ann Med Res 2020;27:780-3.
  • 13. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017;37:1483-503.
  • 14. Zhang S, Chiang GC, Magge RS, Fine HA, Ramakrishna R, Chang EW, et al. Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas. Eur Radiol 2019;29:2751-9.
  • 15. Razik A, Goyal A, Sharma R, Kandasamy D, Seth A, Das P, et al. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma. Br J Radiol. 2020;93:20200569.
  • 16. Shin HJ, Kwak JY, Lee E, Lee MJ, Yoon H, Han K, et al. Texture analysis to differentiate malignant renal tumors in children using gray-scale ultrasonography images. Ultrasound Med Biol 2019;45:2205-12.
  • 17. Kassner A, Thornhill RE. Texture analysis: a review of neurologic MR imaging applications. Am J Neuroradio 2010;31:809-16.
  • 18. Liu HJ, Zhou HF, Zong LX, Liu MQ, Wei SH, Chen ZY. MRI histogram texture feature analysis of the optic nerve in the patients with optic neuritis. Chin Med Sci J 2019;34:18-23.
  • 19. Rizzo JF, III, Lessell S. Risk of developing multiple sclerosis after uncomplicated optic neuritis: a long-term prospective study. Neurology 1988;38:185-90.
  • 20. Francis DA, Compston DA, Batchelor JR, McDonald WI. A reassessment of the risk of multiple sclerosis developing in patients with optic neuritis after extended follow-up. J Neurol Neurosurg Psychiatry 1987;50:758-65.
  • 21. Leocani L, Guerrieri S, Comi G. Visual evoked potentials as a biomarker in multiple sclerosis and associated optic neuritis. J Neuroophthalmol 2018;38:350-7.
  • 22. Chen Z, Lou X, Liu M, Huang D, Wei S, Yu S, et al. Assessment of optic nerve impirment in patients with neuromyelitis optica by MR diffusion tensor imaging. PLoS One 2015;10:e0126574.

Evaluation of optic nerve by histogram analysis on MRI in patients with isolated optic neuritis

Year 2023, Volume: 9 Issue: 2, 285 - 290, 04.03.2023
https://doi.org/10.18621/eurj.1073259

Abstract

Objectives: We aimed to evaluate the Magnetic Resonance Imaging (MRI) histogram texture analyzis of the optic nerve by comparing patients of isolated optic neuritis with a healthy control group and to provide objective information without using contrast in the diagnosis of the disease.

Methods: A total of 40 patients, including 20 patients with isolated optic neuritis (13 females, 7 males) and 20 healthy controls (11 females, 9 males), were included in the study. Non-contrast brain MR images of the patient and control groups were analyzed retrospectively. In the coronal T2-weighted MRI sequence of both groups, the Region of Interest (ROI) was placed in the extraocular anterior 1/3 of the optic nerve of both eyes. Numerical data were obtained using histogram analysis and the data were evaluated in the MATLAB program. The data were compared statistically. In addition, sensitivity and specificity were determined by Receiver Operating Characteristic (ROC) curve analysis.

Results: As a result of histogram analysis, a significant difference was found between the mean values in the healthy and affected eye of the patients with isolated optic neuritis and the mean values of the control group (p < 0.05). A significant difference was found in standard deviation, minimum, maximum, median, variance values between both groups. ROC analysis was performed for mean value, AUC = 0.943 and when threshold value was selected as 354.258 Haunsfield Unit, two groups could be differentiated with 84.2% of sensitivity and 92.1% of specificity. We can say that patients with isolated optic neuritis also have histological effects on the clinically asymptomatic eye.


Conclusions:
Histogram analysis can be used in the diagnosis of the patients with isolated optic neuritis without the need to use contrast in their MRI. In addition, histological effect can be detected in the eye that does not show clinical symptoms with histogram analysis.

References

  • 1. Kanda T, Miyazaki A, Zeng F, Ueno Y, Sofue K, Maeda T, et al. Magnetic resonance imaging of intraocular optic nerve disorders: review article. Pol J Radiol 2020;85:e67-81.
  • 2. Dooley MC, Foroozan R. Optic neuritis. J Ophthalmic Vis Res 2010;5:182-7.
  • 3. Hoorbakht H, Bagherkashi F. Optic neuritis, its differential diagnosis and management. Open Ophthalmol J 2012;6:65-72.
  • 4. Bennett JL. Optic Neuritis. Continuum (Minneap Minn) 2019;25:1236-64.
  • 5. Petzold A, Wattjes MP, Costello F, Flores-Rivera J, Fraser CL, Fujihara K, et al. The investigation of acute optic neuritis: a review and proposed protocol. Nat Rev Neurol 2014;10:447-58.
  • 6. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol 2004;59:1061-9.
  • 7. Jian ZC, Long JF, Liu YJ, Hu XD, Liu JB, Shi XQ, et al. Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis. World J Clin Cases 2019;7:1122-32.
  • 8. Kwon MR, Shin JH, Hahn SY, Oh YL, Kwak JY, Lee E, et al. Histogram analysis of grey scale sonograms to differentiate between the subtypes of follicular variant of papillary thyroid cancer. Clin Radiol 2018;73:591.e1-591.e7.
  • 9. Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: a review. J Magn Reson Imaging 2019;49:927-38.
  • 10. Cauley KA, Fielden SW. A Radiodensity histogram study of the brain in multiple sclerosis. Tomography 2018;4:194-203.
  • 11. Chabat F, Yang GZ, Hansell DM. Obstructive lung diseases: texture classification for differentiation at CT. Radiology 2003;228:871-7.
  • 12. Dogan A, Baykara M. The evaluation of the optic nerve in multiple sclerosis using MRI histogram analysis. Ann Med Res 2020;27:780-3.
  • 13. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017;37:1483-503.
  • 14. Zhang S, Chiang GC, Magge RS, Fine HA, Ramakrishna R, Chang EW, et al. Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas. Eur Radiol 2019;29:2751-9.
  • 15. Razik A, Goyal A, Sharma R, Kandasamy D, Seth A, Das P, et al. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma. Br J Radiol. 2020;93:20200569.
  • 16. Shin HJ, Kwak JY, Lee E, Lee MJ, Yoon H, Han K, et al. Texture analysis to differentiate malignant renal tumors in children using gray-scale ultrasonography images. Ultrasound Med Biol 2019;45:2205-12.
  • 17. Kassner A, Thornhill RE. Texture analysis: a review of neurologic MR imaging applications. Am J Neuroradio 2010;31:809-16.
  • 18. Liu HJ, Zhou HF, Zong LX, Liu MQ, Wei SH, Chen ZY. MRI histogram texture feature analysis of the optic nerve in the patients with optic neuritis. Chin Med Sci J 2019;34:18-23.
  • 19. Rizzo JF, III, Lessell S. Risk of developing multiple sclerosis after uncomplicated optic neuritis: a long-term prospective study. Neurology 1988;38:185-90.
  • 20. Francis DA, Compston DA, Batchelor JR, McDonald WI. A reassessment of the risk of multiple sclerosis developing in patients with optic neuritis after extended follow-up. J Neurol Neurosurg Psychiatry 1987;50:758-65.
  • 21. Leocani L, Guerrieri S, Comi G. Visual evoked potentials as a biomarker in multiple sclerosis and associated optic neuritis. J Neuroophthalmol 2018;38:350-7.
  • 22. Chen Z, Lou X, Liu M, Huang D, Wei S, Yu S, et al. Assessment of optic nerve impirment in patients with neuromyelitis optica by MR diffusion tensor imaging. PLoS One 2015;10:e0126574.
There are 22 citations in total.

Details

Primary Language English
Subjects Radiology and Organ Imaging
Journal Section Original Articles
Authors

Seda Nida Karaküçük 0000-0002-3789-6571

Adil Doğan 0000-0002-2501-1852

Murat Baykara 0000-0003-2588-9013

Selma Urfalıoglu 0000-0002-3709-6988

Publication Date March 4, 2023
Submission Date February 24, 2022
Acceptance Date July 18, 2022
Published in Issue Year 2023 Volume: 9 Issue: 2

Cite

AMA Karaküçük SN, Doğan A, Baykara M, Urfalıoglu S. Evaluation of optic nerve by histogram analysis on MRI in patients with isolated optic neuritis. Eur Res J. March 2023;9(2):285-290. doi:10.18621/eurj.1073259

e-ISSN: 2149-3189 


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