AI and Neurological Rare & Neuroimmune Diseases ── Basic research ── 2026-10-28
Neurodegeneration and neuroimmunology research and AI
- Abstract
PURPOSE: To assess the feasibility of an OCT-based radiomics approach in optic neuritis (ON) and determine whether peripapillary retinal nerve fiber layer (pRNFL) OCT-derived radiomic features could help differentiate between multiple sclerosis-associated ON (MS-ON) and myelin oligodendrocyte glycoprotein antibody-associated disease ON (MOGAD-ON). DESIGN: Retrospective cohort study. PARTICIPANTS: Patients with ON associated with either MS (MS-ON) or MOGAD-ON were selected from the existing Optic Neuropathies of la Timone cohort. In total, 45 patients (61 eyes with ON; 28 with MS and 17 with MOGAD) contributing 266 OCT examinations were included. METHODS: A radiomics pipeline was applied to pRNFL OCT data to extract quantitative imaging features. A total of 102 radiomic features were generated and reduced using feature selection methods. Unsupervised and supervised machine-learning approaches were used to identify relevant radiomic patterns and evaluate their ability to discriminate MS-ON from MOGAD-ON. Models were trained using radiomic features alone, clinical variables alone, and a combination of both. MAIN OUTCOME MEASURES: The primary outcome measure was the diagnostic performance of machine-learning models in distinguishing MS-ON from MOGAD-ON, assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: Among the 102 extracted radiomic features, 62 were retained after feature reduction, and 3 features were selected through machine-learning-based analysis. Supervised machine-learning models (logistic regression) using radiomic features outperformed models based on clinical variables alone (AUC, 0.763 vs. 0.584; P = 0.001). Combining radiomics with clinical variables yielded a numerically higher AUC of 0.788 than radiomics alone (0.763), but without statistical significance ( P = 0.361). The combined model remained significantly better than the clinical-variable-only model ( P < 0.001). CONCLUSIONS: This study establishes a reproducible OCT radiomics pipeline and demonstrates the feasibility of applying unsupervised and supervised machine-learning approaches to pRNFL OCT data. Radiomic features show promising potential for improving the differentiation between MOGAD-ON and MS-ON. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Journal IF-equivalent: 3.3 (OpenAlex 2-year mean citedness, value as of 2026-10-26, retrieved 2026-10-28; not the official Clarivate IF)Reference: El Mohyne H, David T, Mairot K, Demortière S, Appukuttan S, Borrelli M, et al. OCT-Derived Radiomic Features for Differentiating Optic Neuritis Etiologies. Ophthalmology Science. 2026 Nov;6(11):101401. doi:10.1016/j.xops.2026.101401.Checked: Abstract only