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Clinical Classification of Radiation Maculopathy as a Predictor of Functional Response to Intravitreal Dexamethasone
Raffaele Parrozzani1, Samuele Gava1, Carolina Molin1
1Department of Neuroscience-Ophthalmology, University of Padova, 35128 Padova, Italy.
Abstract:
Background: Radiation maculopathy (RM) is a common complication after radiotherapy for intraocular tumors, causing permanent visual loss. Intravitreal dexamethasone (IV DEX) is effective for macular edema (ME) resolution, but functional outcomes remain highly heterogeneous. This study aimed to assess the prognostic utility of CEA classification on visual outcomes following IV DEX based on three parameters: largest cyst diameter (C), ellipsoid zone (EZ) disruption (E) and retinal pigment epithelium (RPE) atrophy (A). Methods: A retrospective analysis of 50 patients with RM secondary to Iodine-125 brachytherapy treated with IV DEX was performed. Best-corrected visual acuity (BCVA) and CEA parameters were analyzed with OCT before and after treatment. Results: IV DEX induced significant anatomical improvement (mean Δcyst: -203.3 μm, p < 0.001), regardless of functional response. Visual outcomes were related to baseline CEA stratification: eyes with intact outer retina gained +6.4 ETDRS letters (p = 0.002); eyes with EZ disruption demonstrated stability (-2.8 letters; p = 0.183); eyes with RPE atrophy significantly worsened (-11.7 letters; p = 0.014). Baseline RPE atrophy was the strongest negative predictor (p = 0.007). Subgroup analysis of patients with intact outer retina identified a baseline BCVA cutpoint of ≤70 letters to predict clinically significant visual improvement (≥5 letters), demonstrating a functional ceiling effect. Conclusions: The CEA classification effectively stratifies functional response in RM. EZ disruption and RPE atrophy identify eyes unlikely to achieve visual improvement despite a reduction in ME, aiding clinical decision-making and establishing functional expectations while promoting a fundamental shift from an edema-driven management to a biomarker-centered approach.