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OCT-Derived Radiomic Features for Differentiating Optic Neuritis Etiologies: An Unsupervised and Supervised
Hoda El Mohyne1, Thierry David2,3, Kevin Mairot2,4
1Aix Marseille Université, Centre d'Exploration Métabolique par Résonance Magnétique-Center for Magnetic Resonance in Biology and Medicine, Centre National de la Recherche Scientifique, Marseille, France.
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.