MRI histogram analysis of optic nerves in children with type 1 neurofibromatosis

Year 2022, Volume: 6 Issue: 1, 68 - 71, 01.01.2022

Yeşim Eroğlu Murat Baykara

https://doi.org/10.28982/josam.990310

Abstract

Background/Aim: Type 1 neurofibromatosis (NF1) is the most common neurocutaneous disease affecting numerous systems. Optic pathway glioma (OPG) is a common tumor in children with NF1 and often has variable clinical presentations. In this study, histogram analysis parameters of optic nerves were measured in the magnetic resonance images (MRI) of children with NF1 and compared with a control group.
Methods: This case-control study consisted of three groups: Ten patients with NF1 without optic pathway glioma (bilateral optic nerve, n: 20), four patients with NF1 with bilateral optic pathway glioma (n: 8), and nineteen healthy controls (n: 38). ROIs were placed on bilateral pre-chiasmatic optic nerves in the images. With histogram analysis, average gray level intensity (mean), the standard deviation, minimum, median, and maximum intensity, uniformity, entropy, kurtosis, variance, skewness, size% M, size% U, size% L, and percentiles were measured.
Results: Mean, median, 3%, 5%, 10%, 25%, and 75% values were higher in NF1 patients with optic pathway glioma (NF1-OPG) than in NF1 patients without optic pathway glioma (NF1-woOPG), and the control group (P<0.001). The same values were significantly higher in the NF1-woOPG group compared to the control group (P<0.001). The minimum, maximum, 1%, 90%, 95%, 97%, and 99% values were significantly higher in the NF1-OPG and NF1-woOPG groups than the control group (P<0.001). The entropy value was significantly higher in the NF1-OPG group than the NF1-woOPG and control groups (5.73, 4.93, and 5.25, respectively, P=0.016).
Conclusion: MRI histogram analysis revealed significant differences between NF1-OPG, NF1-woOPG, and healthy individuals in terms of optic nerves. Thus, we think that it can be used to monitor the optic nerves of children with NF1.

Keywords

Type 1 Neurofibromatosis, Image processing, Magnetic resonance imaging, Optic nerve

Supporting Institution

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References

• 1. Campen CJ, Gutmann DH. Optic Pathway Gliomas in Neurofibromatosis Type 1. J Child Neurol. 2018;33(1):73-81. doi: 10.1177/0883073817739509.
• 2. Guillamo JS, Créange A, Kalifa C, Grill J, Rodriguez D, Doz F, et al. Prognostic factors of CNS tumours in Neurofibromatosis 1 (NF1): a retrospective study of 104 patients. Brain. 2003;126(Pt 1):152-60. doi: 10.1093/brain/awg016.
• 3. Binning MJ, Liu JK, Kestle JR, Brockmeyer DL, Walker ML. Optic pathway gliomas: a review. Neurosurg Focus. 2007;23(5):E2. doi: 10.3171/FOC-07/11/E2.
• 4. Jost SC, Ackerman JW, Garbow JR, Manwaring LP, Gutmann DH, McKinstry RC. Diffusion-weighted and dynamic contrast-enhanced imaging as markers of clinical behavior in children with optic pathway glioma. Pediatr Radiol. 2008;38(12):1293-9. doi: 10.1007/s00247-008-1003-x.
• 5. Aerts HJ, Bussink J, Oyen WJ, van Elmpt W, Folgering AM, Emans D, et al. Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation. Lung Cancer. 2012;75(1):73-6. doi: 10.1016/j.lungcan.2011.06.003.
• 6. McLaren CE, Chen WP, Nie K, Su MY. Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol. 2009;16(7):842-51. doi: 10.1016/j.acra.2009.01.029.
• 7. Baykara M, Koca TT, Demirel A, Berk E. Magnetic resonance imaging evaluation of the median nerve using histogram analysis in carpal tunnel syndrome. Neurological Sciences and Neurophysiology. 2018;35(3):145-50. doi:10.5152/NSN.2018.11280.
• 8. Colombi D, Dinkel J, Weinheimer O, Obermayer B, Buzan T, Nabers D, et al. Visual vs Fully Automatic Histogram-Based Assessment of Idiopathic Pulmonary Fibrosis (IPF) Progression Using Sequential Multidetector Computed Tomography (MDCT). PLoS One. 2015;10(6):e0130653. doi: 10.1371/journal.pone.0130653.
• 9. Molina D, Pérez-Beteta J, Luque B, Arregui E, Calvo M, Borrás JM, et al. Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival. Br J Radiol. 2016;89(1064):20160242. doi: 10.1259/bjr.20160242.
• 10. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004 Dec;59(12):1061-9. doi: 10.1016/j.crad.2004.07.008.
• 11. Raus I, Coroiu RE, Capusan CS. Neuroimaging in pediatric phakomatoses. An educational review. Clujul Med. 2016;89(1):56-64. doi: 10.15386/cjmed-417.
• 12. Baykara S, Baykara M, Mermi O, Yildirim H, Atmaca M. Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorder. Turk J Med Sci. 2021;51(1):140-7. doi: 10.3906/sag-2004-252.
• 13. Yildirim M, Baykara M. Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma. Acta Neurol Belg. 2021 Feb 8. doi: 10.1007/s13760-021-01607-3.
• 14. Yildirim M, Baykara M. Differentiation of Multiple Myeloma and Lytic Bone Metastases: Histogram Analysis. J Comput Assist Tomogr. 2020;44(6):953-5. doi: 10.1097/RCT.0000000000001086.
• 15. Ganeshan B, Miles KA, Young RC, Chatwin CR. Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol. 2009;70(1):101-10. doi: 10.1016/j.ejrad.2007.12.005.
• 16. Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol. 2012;22(4):796-802. doi: 10.1007/s00330-011-2319-8.
• 17. Arslan A. Carotid intima-media thickness and cardiac functions in children with neurofibromatosis type 1. J Surg Med. 2019;3(7):525-7. doi: 10.28982/josam.595760.
• 18. Prada CE, Hufnagel RB, Hummel TR, Lovell AM, Hopkin RJ, Saal HM, et al. The Use of Magnetic Resonance Imaging Screening for Optic Pathway Gliomas in Children with Neurofibromatosis Type 1. J Pediatr. 2015;167(4):851-6.e1. doi: 10.1016/j.jpeds.2015.07.001.
• 19. Lambron J, Rakotonjanahary J, Loisel D, Frampas E, De Carli E, Delion M, et al. Can we improve accuracy and reliability of MRI interpretation in children with optic pathway glioma? Proposal for a reproducible imaging classification. Neuroradiology. 2016;58(2):197-208. doi: 10.1007/s00234-015-1612-7.
• 20. Zahavi A, Toledano H, Cohen R, Sella S, Luckman J, Michowiz S, et al. Use of Optical Coherence Tomography to Detect Retinal Nerve Fiber Loss in Children With Optic Pathway Glioma. Front Neurol. 2018;9:1102. doi: 10.3389/fneur.2018.01102.
• 21. Yeom KW, Lober RM, Andre JB, Fisher PG, Barnes PD, Edwards MS, et al. Prognostic role for diffusion-weighted imaging of pediatric optic pathway glioma. J Neurooncol. 2013;113(3):479-83. doi: 10.1007/s11060-013-1140-4.
• 22. Radulescu E, Ganeshan B, Shergill SS, Medford N, Chatwin C, Young RC, et al. Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia. Psychiatry Res. 2014;223(3):179-86. doi: 10.1016/j.pscychresns.2014.05.014.
• 23. Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK. Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward. Korean J Radiol. 2020;21(12):1345-54. doi: 10.3348/kjr.2020.0715.
• 24. Chen Q, Xia T, Zhang M, Xia N, Liu J, Yang Y. Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges. Aging Dis. 2021;12(1):143-54. doi: 10.14336/AD.2020.0421.
• 25. Kassner A, Thornhill RE. Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol. 2010;31(5):809-16. doi: 10.3174/ajnr.A2061.
• 26. Chekouo T, Mohammed S, Rao A. A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas. Neuroimage Clin. 2020;28:102437. doi: 10.1016/j.nicl.2020.102437.
• 27. Baykara M, Sagiroglu S. An evaluation of magnetic resonance imaging with histogram analysis in patients with idiopathic subjective tinnitus. North Clin Istanb. 2018;6(1):59-63. doi: 10.14744/nci.2018.72593.
• 28. Dogan A, Baykara M. The evaluation of the optic nerve in multiple sclerosis using MRI histogram analysis. Ann Med Res. 2020;27(3):780-3.
• 29. 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(1):18-23. doi: 10.24920/003507.