Dataset Information

Using the Thickness Map from Macular Ganglion Cell Analysis to Differentiate Retinal Vein Occlusion from Glaucoma.

ABSTRACT: PURPOSE:We hypothesized that the thickness map from macular ganglion cell analysis (GCA) acquired from spectral-domain optical coherence tomography can be used to differentiate retinal vein occlusion (RVO) from glaucoma. METHODS:In this retrospective case control study, 37 patients with resolved RVO and 74 patients with primary open-angle glaucoma (POAG) were enrolled. Two independent examiners diagnosed patients with RVO or POAG based on the topographic pattern in the GCA thickness map. Inter-observer agreement for a decision between RVO and POAG was assessed using kappa statistics. Diagnostic specificity and accuracy were calculated. RESULTS:Inter-observer agreement was good, with a kappa value of 0.765 (95% confidence interval, 0.634-0.896, p < 0.001). The diagnostic specificity of RVO from POAG using the GCA thickness map was 93.2% and diagnosis accuracy was 80.4%. CONCLUSIONS:An irregular GCA thickness map represents a simple and convenient differential diagnostic clue to distinguish RVO from POAG.

SUBMITTER: Lee NH

PROVIDER: S-EPMC7602489 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Using the Thickness Map from Macular Ganglion Cell Analysis to Differentiate Retinal Vein Occlusion from Glaucoma.

Lee Nam Ho NH Park Kee Sup KS Lee Han Min HM Kim Jung Yeul JY Kim Chang-Sik CS Kim Kyoung Nam KN

Journal of clinical medicine 20201014 10

<h4>Purpose</h4>We hypothesized that the thickness map from macular ganglion cell analysis (GCA) acquired from spectral-domain optical coherence tomography can be used to differentiate retinal vein occlusion (RVO) from glaucoma.<h4>Methods</h4>In this retrospective case control study, 37 patients with resolved RVO and 74 patients with primary open-angle glaucoma (POAG) were enrolled. Two independent examiners diagnosed patients with RVO or POAG based on the topographic pattern in the GCA thickne ...[more]

PMID: 33066685

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Project description:ImportanceMacular edema (ME) is the leading cause of decreased visual acuity (VA) associated with retinal vein occlusion (RVO). Identifying factors associated with better outcomes in RVO eyes treated with anti-vascular endothelial growth factor (VEGF) therapy may provide information useful in counseling patients.ObjectiveTo investigate baseline characteristics associated with 6-month VA and central subfield thickness (CST) outcomes in participants in the Study of Comparative Treatments for Retinal Vein Occlusion 2 (SCORE2).Design, setting, and participantsA total of 362 patients with central RVO or hemi-RVO were enrolled between September 17, 2014, and November 18, 2015, and randomized 1:1 in a masked fashion to receive bevacizumab or aflibercept. At month 6, 348 participants (96%) had VA outcomes measured and 335 participants (93%) had spectral domain optical coherence tomography outcomes measured. The current data analysis was conducted from February 27, 2017, to April 7, 2017.InterventionsEyes were randomly assigned to receive an intravitreal injection of bevacizumab, 1.25 mg, or aflibercept, 2.0 mg, at baseline and every 4 weeks, with the primary outcome measured at 6 months.Main outcomes and measuresChange from baseline in VA letter score (VALS), VALS gain of 15 or more, change from baseline in CST, CST less than 300 µm, and resolution of ME. Baseline factors associated with 6-month outcome at the 0.05 level in univariate regressions were included in multivariate regressions, with those significant after multiplicity control by the Hochberg method reported.ResultsThe mean (SD) age of patients was 69 (12) years, and 43% were women. Younger patient age (odds ratio [OR], 0.95 per year of age; 95% CI, 0.93-0.98; P = .007) and lower baseline VALS (OR, 0.96 per letter; 95% CI, 0.94-0.98; P < .001) were associated with a 6-month VALS gain of 15 or greater. Compared with bevacizumab, aflibercept treatment was associated with a higher odds of ME resolution (OR, 3.59; 95% CI, 2.22-5.80; P < .001) and CST less than 300 µm (OR, 5.30; 95% CI, 2.40-11.67; P = .001), but not with a better VA outcome. Macular edema was less likely to resolve in eyes that received anti-VEGF treatment prior to study participation (OR, 0.33; 95% CI, 0.17-0.64; P = .03).Conclusions and relevanceIn eyes treated with bevacizumab or aflibercept, younger age and worse baseline VALS were associated with better 6-month VA outcomes. Aflibercept treatment was associated with more favorable spectral domain optical coherence tomography outcomes but not VA outcomes. These findings may be useful in assessing expected response at month 6 after monthly injection of anti-VEGF agents for treating ME due to CRVO and HRVO.Trial registrationclinicaltrials.gov Identifier: NCT01969708.

Project description:PurposeTo examine whether deep-learning denoised optical coherence tomography angiography (OCTA) images could enhance automated macular ischemia quantification in branch retinal vein occlusion (BRVO).MethodsThis retrospective, single-center, cross-sectional study enrolled 74 patients with BRVO and 46 age-matched healthy subjects. The severity of macular ischemia was graded as mild, moderate, or severe. Denoised OCTA images were produced using a neural network model. Quantitative parameters derived from denoised images, including vessel density and nonperfusion area, were compared with those derived from the OCTA machine. The main outcome measures were correlations between quantitative parameters, and areas under receiver operating characteristic curves (AUCs) in classifying the severity of the macular ischemia.ResultsThe vessel density and nonperfusion area from denoised images were correlated strongly with the corresponding parameters from machine-derived images in control eyes and BRVO eyes with mild or moderate macular ischemia (all P < 0.001). However, no such correlation was found in eyes with severe macular ischemia. The vessel density and nonperfusion area from denoised images had significantly larger area under receiver operating characteristic curve than those derived from the original images in classifying moderate versus severe macular ischemia (0.927 vs 0.802 [P = 0.042] and 0.946 vs 0.797, [P = 0.022], respectively). There were no significant differences in the areas under receiver operating characteristic curve between the denoised images and the machine-derived parameters in classifying control versus BRVO, and mild versus moderate macular ischemia.ConclusionsA neural network model is useful for removing speckle noise on OCTA images and facilitating the automated grading of macular ischemia in eyes with BRVO.Translational relevanceDeep-learning denoised optical coherence tomography angiography images could enhance automated macular ischemia quantification.

Dataset Information

Using the Thickness Map from Macular Ganglion Cell Analysis to Differentiate Retinal Vein Occlusion from Glaucoma.

Publications

Using the Thickness Map from Macular Ganglion Cell Analysis to Differentiate Retinal Vein Occlusion from Glaucoma.

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