Exploratory Radiomic Analysis of Anterior Segment OCT Images in Patients with Vitreoretinal Lymphoma

Purpose To explore the ability of a radiomics-based classification model of identifying anterior segment optical coherence tomography (AS-OCT) image features compatible with active vitreoretinal lymphoma (VRL). The secondary aim was to investigate the possible association between these image features and aqueous humour molecular biomarkers used in VRL assessment.Methods We conducted a retrospective single-centre study of patients with biopsy-proven VRL, and at least one series of baseline or follow-up AS-OCT image scans and an aqueous tap. We extracted radiomic features from a manually delineated region of interest (ROI) in anterior vitreous AS-OCT images, five were selected and trained XGBoost classifiers with leave-two-patients-out cross-validation (L2PO) to distinguish active from inactive disease, using AUC-ROC as performance metric. If present, interleukin (IL)-10:IL-6 ratios were correlated with imaging features using logistic regression analyses.Results Eight patients (22 visits total, 43 eyes) were analysed. From the L2PO analysis, we obtained an AUC metric of 0.74 with a 95% confidence interval (CI) of 0.68–0.81. GLRLM_LRLGE was the feature most consistently associated with disease activity. When IL-10:IL-6 > 1 was used as the outcome, the association weakened (all p-values > 0.05), though mean and median values showed the same direction of effect.Conclusions This pilot study explored the potential innovative role of radiomics to analyse VRL activity status using AS-OCT images. Although it is currently far from being a clinical tool, radiomic analysis could be the subject of future investigations aiming to refine non-invasive monitoring strategies. Prospective studies with standardized, simultaneous serial imaging and aqueous tap are required.

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Journal
Ocular Immunology and Inflammation
Published
2026-10-05
DOI
https://doi.org/10.1080/09273948.2026.2739454
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Exploratory Radiomic Analysis of Anterior Segment OCT Images in Patients with Vitreoretinal Lymphoma

Pietro Gentile, Mirco Braghiroli, Alessandro Zerbini, Lucia Belloni et al.
Ocular Immunology and Inflammation
Radiomics and Machine Learning in Medical Imaging
article

Exploratory Radiomic Analysis of Anterior Segment OCT Images in Patients with Vitreoretinal Lymphoma

Pietro Gentile, Mirco Braghiroli, Alessandro Zerbini, Lucia Belloni, Alberto Cavazza, Matteo Belpoliti, Fabrizio Gozzi, Marco Vecchi, Stefano Ricci, Massimo Vicentini, S. Luminari, Emanuele Ragusa, Areti Kosmarikou, Marco Bertolini, Laura Verzellesi, Paolo Giorgi Rossi, Fiorella Ilariucci, Alberto Bavieri, Luca De Simone, Riccardo Valli, Mauro Iori, Francesco Merli, Stefania Croci, Enrico Farnetti, Valeria Trojani, Davide Nicoli, Pamela Mancuso, Elena Bolletta, Michele De Maria, Magda Zanelli, Luca Cimino, Letizia Bartolini, Martina Fantuzzi, Emma Celeo
article en

Abstract

Purpose To explore the ability of a radiomics-based classification model of identifying anterior segment optical coherence tomography (AS-OCT) image features compatible with active vitreoretinal lymphoma (VRL). The secondary aim was to investigate the possible association between these image features and aqueous humour molecular biomarkers used in VRL assessment.Methods We conducted a retrospective single-centre study of patients with biopsy-proven VRL, and at least one series of baseline or follow-up AS-OCT image scans and an aqueous tap. We extracted radiomic features from a manually delineated region of interest (ROI) in anterior vitreous AS-OCT images, five were selected and trained XGBoost classifiers with leave-two-patients-out cross-validation (L2PO) to distinguish active from inactive disease, using AUC-ROC as performance metric. If present, interleukin (IL)-10:IL-6 ratios were correlated with imaging features using logistic regression analyses.Results Eight patients (22 visits total, 43 eyes) were analysed. From the L2PO analysis, we obtained an AUC metric of 0.74 with a 95% confidence interval (CI) of 0.68–0.81. GLRLM_LRLGE was the feature most consistently associated with disease activity. When IL-10:IL-6 > 1 was used as the outcome, the association weakened (all p-values > 0.05), though mean and median values showed the same direction of effect.Conclusions This pilot study explored the potential innovative role of radiomics to analyse VRL activity status using AS-OCT images. Although it is currently far from being a clinical tool, radiomic analysis could be the subject of future investigations aiming to refine non-invasive monitoring strategies. Prospective studies with standardized, simultaneous serial imaging and aqueous tap are required.

Ocular Immunology and Inflammation
University of Modena and Reggio Emilia (IT), Azienda Sanitaria Unità Locale di Reggio Emilia (IT)
Openalex Percentile: Top 12%
Radiomics and Machine Learning in Medical Imaging
DOISourceOpenAlex
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