Bridging Imaging and Molecular Biomarkers in Trigeminal Neuralgia: Toward Precision Diagnosis and Prognostication in Neuropathic Pain
Main Article Content
Abstract
Despite advances in imaging and clinical assessment, current diagnostic paradigms lack precision needed to subcategorize disease or predict therapeutic response with accuracy. This review proposes precision neuropathic pain diagnostics through the combination of artificial intelligence-guided imaging and molecular biomarker identification, using the use-case of Trigeminal neuralgia (TN), a refractory craniofacial pain disorder characterized by paroxysmal, one-sided facial pain and variable treatment response. We use trigeminal neuralgia as a case example to explore how deep learning, advanced imaging, and molecular profiling can work together to improve the diagnosis and treatment of neuropathic pain. First, the current paper reviews contemporary advances in deep learning for neuroimaging, particularly convolutional neural networks and U-Net architectures that have enabled automatic segmentation of the trigeminal nerve and paved the way for radiomic measurement of neurovascular compression. Such advances optimize the objectivity of surgery stratification and help to distinguish classical from idiopathic Trigeminal Neuralgia. In parallel, proteomic profiling of cerebrospinal fluid and plasma has revealed TN-specific molecular signatures, including upregulation of inflammatory, stress-related, and axonal damage markers that are different from those of similar disorders such as multiple sclerosis. Both markers not only have implications for disease pathobiology but also for identifying new therapeutic targets. The current study proposes a multimodal data integration platform combining imaging and molecular phenotypes using machine learning and multi-omics platforms. The integration permits mechanistic subtyping and predictive modeling of treatment response, with potential applications expanding to diabetic neuropathy and complex regional pain syndrome. Clinical deployment challenges, such as heterogeneity of data, ambiguity regarding regulation, and ethical risk, are addressed along with near-term solutions such as federated learning and interoperable biomarker registries. At the intersection of neurosurgery, radiology, and computational science, Trigeminal Neuralgia offers a scalable model for precision pain medicine, transforming care through mechanism-based classification and patient-stratified interventions.
Keywords:
Trigeminal Neuralgia, Neuropathic Pain, Precision Medicine, Deep Learning, Biomarkers, Multimodal Integration
Article Details
How to Cite
ABIKENARI, Matthew et al.
Bridging Imaging and Molecular Biomarkers in Trigeminal Neuralgia: Toward Precision Diagnosis and Prognostication in Neuropathic Pain.
Medical Research Archives, [S.l.], v. 13, n. 5, may 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/6605>. Date accessed: 21 june 2025.
doi: https://doi.org/10.18103/mra.v13i5.6605.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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11. Obermann, Mark, and Zaza Katsarava. "Update on trigeminal neuralgia." Expert review of neurotherapeutics 9.3 (2009): 323-329.
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29. Kolakowski, Lukasz, et al. "Interdisciplinary strategies for diagnosis and treatment of trigeminal neuralgia." Swiss Medical Weekly 154.7 (2024): 3460.
30. Zakrzewska JM, Wu J, Mon-Williams M, et al. Evaluating the impact of orofacial pain on quality of life using a cross-sectional survey in primary care. Pain. 2013;154(10):2240-2247.
31. Witkin, J. M., Pandey, K. P. & Smith, J. L. Clinical investigations of compounds targeting metabotropic glutamate receptors. Pharmacol. Biochem. Behav. 219, 173446 (2022).
32. Halbert-Elliott, Kyra M., et al. "Deep learning–based segmentation of the trigeminal nerve and surrounding vasculature in trigeminal neuralgia." Journal of Neurosurgery 1.aop (2025): 1-9.
33. Doshi TL, Nixdorf DR, Campbell CM, Raja SN. Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain. Can J Pain. 2020;4(1): 1-18. https://doi:10.1080/24740527.2019.1709163
34. Wu C, Chen Y, Liu J, et al. Automated segmentation of trigeminal nerve using U-net in MR images. Comput Methods Programs Biomed. 2021;200:105854.
35. Zhang H, Bai Y, Zhu J, et al. Deep learning based lumbar spinal nerve root classification using MRI. J Magn Reson Imaging. 2020;52(3):874-883.
36. Hwang, Jun Ho, et al. "Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia." Life 14.3 (2024): 355.
37. Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information. 2025; 16(3):195.
https://doi.org/10.3390/info16030195
38. Jamalabadi H, Kaur N, Navab N, et al. Deep learning for diagnosis and risk stratification of diabetic neuropathy using multimodal data. Sci Rep. 2022;12(1):7894.
39. Tran AT, Zeevi T, Payabvash S. Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging. BioMedInformatics. 2025; 5(2):20. https://doi.org/10.3390/biomedinformatics5020020
40. Bielewicz J, Kamieniak M, Szymoniuk M, Litak J, Czyżewski W, Kamieniak P. Diagnosis and Management of Neuropathic Pain in Spine Diseases. Journal of Clinical Medicine. 2023; 12(4):1380. https://doi.org/10.3390/jcm12041380
41. Xu, Dongyuan, et al. "Elucidating molecular lipid perturbations in trigeminal neuralgia using cerebrospinal fluid lipidomics." Scientific Reports 15.1 (2025): 11777
42. Lafta R, van Munster C, Grootenboer H, et al. Proteomic analysis of trigeminal neuralgia patients reveals distinct molecular signatures and therapeutic implications. J Neuroinflammation. 2023;20(1):55.
43. Goebel A, Baranowski A, Maurer K, et al. Elevated serum cytokines in patients with complex regional pain syndrome. Pain. 2005;115(1-2):243-249.
44. Callaghan BC, Price RS, Feldman EL. Distal symmetric polyneuropathy: a review. JAMA. 2015; 314(20):2172-2181.
45. Shi X, Guo TZ, Li WW, et al. C5a complement and cytokine signaling mediate the pronociceptive effects of complex regional pain syndrome patient IgM in fracture mice. Pain. 2021;162(5):1400-1415. https://doi:10.1097/j.pain.0000000000002150
46. Wang, H., et al. "Chronic neuropathic pain is accompanied by global changes in gene expression and shares pathobiology with neurodegenerative diseases." Neuroscience 114.3 (2002): 529-546.
47. Rueda‐Carrasco, Javier, et al. "SFRP1 modulates astrocyte‐to‐microglia crosstalk in acute and chronic neuroinflammation." EMBO reports 22.11 (2021): e51696
48. Maiarù M, Tochiki KK, Cox MB, et al. The stress regulator FKBP51 drives chronic pain by modulating spinal glucocorticoid signaling. Sci Transl Med. 2016;8(325):325ra19. https://doi:10.1126/scitranslmed.aab3376
49. Siddarth, Prabha, et al. "Inflammatory markers of geriatric depression response to Tai Chi or health education adjunct interventions." The American Journal of Geriatric Psychiatry 31.1 (2023): 22-32.
50. Guo Y, Li H, Hu L, et al. Proteomic profiling of cerebrospinal fluid in trigeminal neuralgia patients reveals axonal damage and neuroinflammation. J Neuroinflammation. 2022;19(1):38. https://doi:10.1186/s12974-022-02313-5
51. Chen ST, Yang JT, Weng HH, Wang HL, Yeh MY, Tsai YH. Diffusion tensor imaging for assessment of microstructural changes associate with treatment outcome at one-year after radiofrequency Rhizotomy in trigeminal neuralgia. BMC Neurol. 2019;19(1):62. Published 2019 Apr 12. https://doi:10.1186/s12883-019-1295-5
52. Uceyler N, Zeller D, Kahn AK, et al. Small fibre pathology in patients with fibromyalgia syndrome. Brain. 2013;136(Pt 6):1857-1867. https://doi:10.1093/brain/awt053
53. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236-1246. https://doi:10.1093/bib/bbx044
54. Ashina, S., Robertson, C.E., Srikiatkhachorn, A. et al. Trigeminal neuralgia. Nat Rev Dis Primers 10, 39 (2024). https://doi.org/10.1038/s41572-024-00523-z
55. Wu, X., Zhang, S., Zhang, Z. et al. Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients. npj Precis. Onc. 8, 181 (2024). https://doi.org/10.1038/s41698-024-00670-2
56. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. https://doi:10.1186/s13059-017-1215-1
57. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. https://doi:10.1038/s41591-018-0300-7
58. Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024;151:110424. https://doi:10.1016/j.p atcog.2024.110424
59. Dayan, I., Roth, H.R., Zhong, A. et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 27, 1735–1743 (2021). https://doi.org/10.1038/s41591-021-01506-3
60. Hagedorn, Jonathan M., et al. "How well do current laboratory biomarkers inform clinical decision-making in chronic pain management?." Journal of Pain Research (2021): 3695-3710.
61. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119. https://doi:10.1038/s41746-020-00323-1
62. Clark SJ, Arneson D, Darnell G, et al. Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol. 2022;23(1):188. https://doi:10.1186/s13059-022-02742-y
63. Diaz MM, Caylor J, Strigo I, et al. Toward Composite Pain Biomarkers of Neuropathic Pain-Focus on Peripheral Neuropathic Pain. Front Pain Res (Lausanne). 2022;3:869215. Published 2022 May 11. https://doi:10.3389/fpain.2022.869215
64. National Institutes of Health. NIH Bridge2AI Program. https://commonfund.nih.gov/bridge2ai
65. Innovative Medicines Initiative. IMI-PainCare Project. https://www.imi-paincare.eu
2. Zakrzewska, Joanna M. "Diagnosis and differential diagnosis of trigeminal neuralgia." The Clinical journal of pain 18.1 (2002): 14-21.
3. Cruccu, Giorgio, et al. "Trigeminal neuralgia: new classification and diagnostic grading for practice and research." Neurology 87.2 (2016): 220-228.
4. Bendtsen, Lars, et al. "Advances in diagnosis, classification, pathophysiology, and management of trigeminal neuralgia." The Lancet Neurology 19.9 (2020): 784-796.
5. Di Stefano, G., Maarbjerg, S., Nurmikko, T., Truini, A. & Cruccu, G. Triggering trigeminal neuralgia. Cephalalgia 38, 1049–1056 (2018).
6. Lambru, Giorgio, Joanna Zakrzewska, and Manjit Matharu. "Trigeminal neuralgia: a practical guide." Practical neurology 21.5 (2021): 392-402.
7. Headache classification committee of the International Headache Society (IHS) the International Classification of Headache Disorders, 3rd edition. Cephalalgia 38, 1–211 (2018).
8. Maarbjerg, S. & Benoliel, R. The changing face of trigeminal neuralgia-a narrative review. Headache 61, 817–837 (2021).
9. Araya, Erika I., et al. "Trigeminal neuralgia: basic and clinical aspects." Current neuropharmacology 18.2 (2020): 109-119.
10. Goadsby, Peter J. "Trigeminal autonomic cephalalgias." CONTINUUM: Lifelong Learning in Neurology 18.4 (2012): 883-895.
11. Obermann, Mark, and Zaza Katsarava. "Update on trigeminal neuralgia." Expert review of neurotherapeutics 9.3 (2009): 323-329.
12. Maarbjerg, S., Gozalov, A., Olesen, J. & Bendtsen, L. Concomitant persistent pain in classical trigeminal neuralgia — evidence for different subtypes. Headache 54, 1173–1183 (2014)
13. Latorre, G., et al. "Diagnosis and treatment of trigeminal neuralgia: Consensus statement from the Spanish Society of Neurology’s Headache Study Group." Neurología (English Edition) (2023).
14. Khawaja, Shehryar Nasir, and Steven J. Scrivani. "Trigeminal neuralgia." Dental Clinics 67.1 (2023): 99-115.
15. Hung, Peter Shih-Ping, et al. "Sex differences in patient journeys to diagnosis, referral, and surgical treatment of trigeminal neuralgia: implications for equitable care." Journal of Neurosurgery 139.2 (2022): 463-471.
16. Maarbjerg, S., Gozalov, A., Olesen, J. & Bendtsen, L. Trigeminal neuralgia — a prospective systematic study of clinical characteristics in 158 patients. Headache 54, 1574–1582 (2014).
17. Allam, Anthony K., et al. "Trigeminal neuralgia: diagnosis and treatment." Neurologic Clinics 41.1 (2023): 107-121.
18. Andersen, A. S. S. et al. Microvascular decompression in trigeminal neuralgia — a prospective study of 115 patients. J. Headache Pain 23, 145 (2022).
19. Bendtsen, L. et al. European Academy of Neurology guideline on trigeminal neuralgia. Eur. J. Neurol. 26, 831–849 (2019).
20. Di Stefano, G., Maarbjerg, S. & Truini, A. Trigeminal neuralgia secondary to multiple sclerosis: from the clinical picture to the treatment options. J. Headache Pain 20, 20 (2019).
21. Bora, Nikita, et al. "A systematic review of the role of magnetic resonance imaging in the diagnosis and detection of neurovascular conflict in patients with trigeminal neuralgia." Cureus 15.9 (2023).
22. Zeng, Chen, et al. "Recent advances of magnetic resonance neuroimaging in trigeminal neuralgia." Current pain and headache reports 25 (2021): 1-7.
23. Obermann, M. et al. Impaired trigeminal nociceptive processing in patients with trigeminal neuralgia. Neurology 69, 835–841 (2007).
24. Jani, R. H. et al. Trigeminal nerve compression without trigeminal neuralgia: intraoperative vs imaging evidence. Neurosurgery 84, 60–65 (2019).
25. Sandell T, Eide PK. Effect of microvascular decompression in trigeminal neuralgia patients with or without constant pain. Neurosurgery. 2008; 63(1):93-99.
26. DeSouza DD, Hodaie M, Davis KD. Abnormal trigeminal nerve microstructure and brain white matter in idiopathic trigeminal neuralgia. Pain. 2014; 155(1):37-44.
27. Eller JL, Raslan AM, Burchiel KJ. Trigeminal neuralgia: definition and classification. Neurosurg Focus. 2005;18(5):E3.
28. Slettebø, Haldor. "Is this really trigeminal neuralgia? Diagnostic re-evaluation of patients referred for neurosurgery." Scandinavian Journal of Pain 21.4 (2021): 788-793.
29. Kolakowski, Lukasz, et al. "Interdisciplinary strategies for diagnosis and treatment of trigeminal neuralgia." Swiss Medical Weekly 154.7 (2024): 3460.
30. Zakrzewska JM, Wu J, Mon-Williams M, et al. Evaluating the impact of orofacial pain on quality of life using a cross-sectional survey in primary care. Pain. 2013;154(10):2240-2247.
31. Witkin, J. M., Pandey, K. P. & Smith, J. L. Clinical investigations of compounds targeting metabotropic glutamate receptors. Pharmacol. Biochem. Behav. 219, 173446 (2022).
32. Halbert-Elliott, Kyra M., et al. "Deep learning–based segmentation of the trigeminal nerve and surrounding vasculature in trigeminal neuralgia." Journal of Neurosurgery 1.aop (2025): 1-9.
33. Doshi TL, Nixdorf DR, Campbell CM, Raja SN. Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain. Can J Pain. 2020;4(1): 1-18. https://doi:10.1080/24740527.2019.1709163
34. Wu C, Chen Y, Liu J, et al. Automated segmentation of trigeminal nerve using U-net in MR images. Comput Methods Programs Biomed. 2021;200:105854.
35. Zhang H, Bai Y, Zhu J, et al. Deep learning based lumbar spinal nerve root classification using MRI. J Magn Reson Imaging. 2020;52(3):874-883.
36. Hwang, Jun Ho, et al. "Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia." Life 14.3 (2024): 355.
37. Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information. 2025; 16(3):195.
https://doi.org/10.3390/info16030195
38. Jamalabadi H, Kaur N, Navab N, et al. Deep learning for diagnosis and risk stratification of diabetic neuropathy using multimodal data. Sci Rep. 2022;12(1):7894.
39. Tran AT, Zeevi T, Payabvash S. Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging. BioMedInformatics. 2025; 5(2):20. https://doi.org/10.3390/biomedinformatics5020020
40. Bielewicz J, Kamieniak M, Szymoniuk M, Litak J, Czyżewski W, Kamieniak P. Diagnosis and Management of Neuropathic Pain in Spine Diseases. Journal of Clinical Medicine. 2023; 12(4):1380. https://doi.org/10.3390/jcm12041380
41. Xu, Dongyuan, et al. "Elucidating molecular lipid perturbations in trigeminal neuralgia using cerebrospinal fluid lipidomics." Scientific Reports 15.1 (2025): 11777
42. Lafta R, van Munster C, Grootenboer H, et al. Proteomic analysis of trigeminal neuralgia patients reveals distinct molecular signatures and therapeutic implications. J Neuroinflammation. 2023;20(1):55.
43. Goebel A, Baranowski A, Maurer K, et al. Elevated serum cytokines in patients with complex regional pain syndrome. Pain. 2005;115(1-2):243-249.
44. Callaghan BC, Price RS, Feldman EL. Distal symmetric polyneuropathy: a review. JAMA. 2015; 314(20):2172-2181.
45. Shi X, Guo TZ, Li WW, et al. C5a complement and cytokine signaling mediate the pronociceptive effects of complex regional pain syndrome patient IgM in fracture mice. Pain. 2021;162(5):1400-1415. https://doi:10.1097/j.pain.0000000000002150
46. Wang, H., et al. "Chronic neuropathic pain is accompanied by global changes in gene expression and shares pathobiology with neurodegenerative diseases." Neuroscience 114.3 (2002): 529-546.
47. Rueda‐Carrasco, Javier, et al. "SFRP1 modulates astrocyte‐to‐microglia crosstalk in acute and chronic neuroinflammation." EMBO reports 22.11 (2021): e51696
48. Maiarù M, Tochiki KK, Cox MB, et al. The stress regulator FKBP51 drives chronic pain by modulating spinal glucocorticoid signaling. Sci Transl Med. 2016;8(325):325ra19. https://doi:10.1126/scitranslmed.aab3376
49. Siddarth, Prabha, et al. "Inflammatory markers of geriatric depression response to Tai Chi or health education adjunct interventions." The American Journal of Geriatric Psychiatry 31.1 (2023): 22-32.
50. Guo Y, Li H, Hu L, et al. Proteomic profiling of cerebrospinal fluid in trigeminal neuralgia patients reveals axonal damage and neuroinflammation. J Neuroinflammation. 2022;19(1):38. https://doi:10.1186/s12974-022-02313-5
51. Chen ST, Yang JT, Weng HH, Wang HL, Yeh MY, Tsai YH. Diffusion tensor imaging for assessment of microstructural changes associate with treatment outcome at one-year after radiofrequency Rhizotomy in trigeminal neuralgia. BMC Neurol. 2019;19(1):62. Published 2019 Apr 12. https://doi:10.1186/s12883-019-1295-5
52. Uceyler N, Zeller D, Kahn AK, et al. Small fibre pathology in patients with fibromyalgia syndrome. Brain. 2013;136(Pt 6):1857-1867. https://doi:10.1093/brain/awt053
53. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236-1246. https://doi:10.1093/bib/bbx044
54. Ashina, S., Robertson, C.E., Srikiatkhachorn, A. et al. Trigeminal neuralgia. Nat Rev Dis Primers 10, 39 (2024). https://doi.org/10.1038/s41572-024-00523-z
55. Wu, X., Zhang, S., Zhang, Z. et al. Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients. npj Precis. Onc. 8, 181 (2024). https://doi.org/10.1038/s41698-024-00670-2
56. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. https://doi:10.1186/s13059-017-1215-1
57. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. https://doi:10.1038/s41591-018-0300-7
58. Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024;151:110424. https://doi:10.1016/j.p atcog.2024.110424
59. Dayan, I., Roth, H.R., Zhong, A. et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 27, 1735–1743 (2021). https://doi.org/10.1038/s41591-021-01506-3
60. Hagedorn, Jonathan M., et al. "How well do current laboratory biomarkers inform clinical decision-making in chronic pain management?." Journal of Pain Research (2021): 3695-3710.
61. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119. https://doi:10.1038/s41746-020-00323-1
62. Clark SJ, Arneson D, Darnell G, et al. Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol. 2022;23(1):188. https://doi:10.1186/s13059-022-02742-y
63. Diaz MM, Caylor J, Strigo I, et al. Toward Composite Pain Biomarkers of Neuropathic Pain-Focus on Peripheral Neuropathic Pain. Front Pain Res (Lausanne). 2022;3:869215. Published 2022 May 11. https://doi:10.3389/fpain.2022.869215
64. National Institutes of Health. NIH Bridge2AI Program. https://commonfund.nih.gov/bridge2ai
65. Innovative Medicines Initiative. IMI-PainCare Project. https://www.imi-paincare.eu