Radiomics Analysis of Ultrasound Images of Peripheral Nerves in Young Patients with Type 1 Diabetes Mellitus in Comparison with Healthy Controls

Background. Early diagnosis of diabetic polyneuropathy in childhood is an urgent healthcare problem. Radiomics analysis of ultrasound images is a promising diagnostic tool for assessing the morphological structure of peripheral nerves in type 1 diabetes mellitus (T1DM).

Objective: to evaluate the possibility of using radiomics analysis in the diagnosis of peripheral nerve changes based on ultrasound images in T1DM patients of young age.

Material and methods. A total of 126 ultrasound images of peripheral nerves of the upper and lower limbs in T1DM patients aged 10–17 years (n=10) and controls (n=10) (four locations, greyscale mode) were studied.

Results. Radiomics analysis revealed differences in the texture of peripheral nerves of the limbs in young T1DM patients when compared with healthy individuals.

Conclusion. The method of radiomics analysis is a promising diagnostic tool for assessing changes in peripheral nerves in children and adolescents with T1DM.

1. Goyal K, Aggarwal P, Gupta M. Ultrasound evaluation of peripheral nerves of the lower limb in diabetic peripheral neuropathy. Eur J Radiol. 2021; 145: 110058. https://doi.org/10.1016/j.ejrad.2021.110058.

2. Tandon A, Khullar T, Maheshwari S, et al. High resolution ultrasound in subclinical diabetic neuropathy: a potential screening tool. Ultrasound. 2021; 29(3): 150–61. https://doi.org/10.1177/1742271X20958034.

3. Kallinikou D, Soldatou A, Tsentidis C, et al. Diabetic neuropathy in children and adolescents with type 1 diabetes mellitus: diagnosis, pathogenesis, and associated genetic markers. Diabetes Metab Res Rev. 2019; 35(7): e3178. https://doi.org/10.1002/dmrr.3178.

4. Sloan G, Selvarajah D, Tesfaye S. Pathogenesis, diagnosis and clinical management of diabetic sensorimotor peripheral neuropathy. Nat Rev Endocrinol. 2021; 17: 400–20. https://doi.org/10.1038/s41574-021-00496-z.

5. Никитин С.С., Муртазина А.Ф., Дружинин Д.С. Блок проведения возбуждения по периферическому нерву как электрофизиологический феномен: обзор литературы. Нервно-мышечные болезни. 2019; 9(1): 12–23. https://doi.org/10.17650/2222-8721-2019-9-1-12-23. [Nikitin SS, Murtazina AF, Druzhinin DS. Conduction block as an electrophysiological phenomenon: a review of the literature. Neuromuscular Diseases. 2019; 9(1): 12–23 (in Russ). https://doi.org/10.17650/2222-8721-2019-9-1-12-23.]

6. Фомина С.В., Завадовская В.Д., Самойлова Ю.Г. и др. Возможности ультразвуковой эластографии периферических нервов у детей с сахарным диабетом 1 типа. Медицинская визуализация. 2024; 28(4): 133–41. https://doi.org/10.24835/1607-0763-1437. [Fomina SV, Zavadovskaya VD, Samoilova IG, et al. Possibilities of ultrasound elastography of peripheral nerves in children with type 1 diabetes mellitus. Medical Visualization. 2024; 28(4): 133–41 (in Russ). https://doi.org/10.24835/1607-0763-1437.]

7. Senarai T, Pratipanawatr T, Yurasakpong L, et al. Cross-sectional area of the tibial nerve in diabetic peripheral neuropathy patients: a systematic review and meta-analysis of ultrasonography studies. Medicina. 2022; 58(12): 1696. https://doi.org/10.3390/medicina58121696.

8. Данилова М.Г., Салтыкова В.Г., Усенко Е.Е., Абоян И.А. Ультразвуковое исследование локтевого нерва у детей различных возрастных групп. Ультразвуковая и функциональная диагностика. 2019; 2: 61–79. https://doi.org/10.24835/1607-0771-2019-2-61-79. [Danilova MG, Saltykova VG, Usenko EE, Aboian IA. Ulnar nerve ultrasound in different age groups children. Ultrasound & Functional Diagnostics. 2019; 2: 61–79 (in Russ). https://doi.org/10.24835/1607-0771-2019-2-61-79.]

9. Hatt M, Le Rest CC, Tixier F, et al. Radiomics: data are also images. J Nucl Med. 2019; 60(Suppl 2): 38S–44S.

10. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14(12): 749–62. https://doi.org/10.1038/nrclinonc.2017.141.

11. McCague C, Ramlee S, Reinius M, et al. Introduction to radiomics for a clinical audience. Clin Radiol. 2023; 78(2): 83– 98. https://doi.org/10.1016/j.crad.2022.08.149.

12. Jiang H, Chen L, Zhao YJ, et al. machine learning-based ultrasomics for predicting subacromial impingement syndrome stages. J Ultrasound Med. 2022; 41(9): 2279–85. https://doi.org/10.1002/jum.15914.

13. Jia Y, Yang J, Zhu Y, et al. Ultrasound-based radiomics: current status, challenges and future opportunities. Med Ultrason. 2022; 24(4): 451–60. https://doi.org/10.11152/mu-3248.

14. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012; 30(9): 1323–41. https://doi.org/10.1016/j.mri.2012.05.001.

15. Van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017; 77(21): e104–7. https://doi.org/10.1158/0008-5472.CAN-17-0339.