Dataset Information

Fundus autofluorescence and spectral-domain optical coherence tomography characteristics in a rapidly progressing form of geographic atrophy.

ABSTRACT: To further characterize a previously described phenotypic variant of geographic atrophy (GA) associated with rapid progression and a diffuse-trickling appearance on fundus autofluorescence (FAF).Thirty-six patients (60 eyes; 72.2% women; mean age, 69.4 ± 10.7 years) with this distinct phenotype were examined by simultaneous confocal scanning laser ophthalmoscopy (cSLO) and spectral-domain optical coherence tomography (SD-OCT) imaging. Images were qualitatively and quantitatively analyzed and compared with 60 eyes (38 patients) with non diffuse-trickling GA.The atrophic area in the diffuse-trickling phenotype showed a grayish FAF signal and characteristic coalescent lobular configuration at the lesion boundaries. SD-OCT revealed a marked splitting of band 4 (the presumptive retinal pigment epithelium (RPE)/Bruch's membrane (BM) complex) in all 240 analyzed border sections of diffuse-trickling GA eyes (four borders/eye) with a mean distance between the inner and outer parts of band 4 of 23.2 ± 7.5 ?m. This finding was present in only 13.8% (33/240) of analyzed border sections in non diffuse-trickling GA.Patients with the rapidly progressing diffuse-trickling GA phenotype exhibited a characteristic marked separation within the RPE/BM complex on SD-OCT-imaging. The presumed histopathologic correlates are basal laminar deposits. Such deposits may promote RPE cell death and, thus, contribute to rapid GA progression. The persistence of these deposits within the atrophic lesion may account for the distinct grayish FAF appearance, which differs from the markedly reduced signal in other forms of GA. Identification of such alterations based on FAF and SD-OCT imaging may be helpful in future interventional trials directed toward slowing GA progression. (ClinicalTrials.gov number, NCT00393692.).

SUBMITTER: Fleckenstein M

PROVIDER: S-EPMC3109052 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Fundus autofluorescence and spectral-domain optical coherence tomography characteristics in a rapidly progressing form of geographic atrophy.

Fleckenstein Monika M Schmitz-Valckenberg Steffen S Martens Christine C Kosanetzky Sebastian S Brinkmann Christian K CK Hageman Gregory S GS Holz Frank G FG

Investigative ophthalmology & visual science 20110601 6

<h4>Purpose</h4>To further characterize a previously described phenotypic variant of geographic atrophy (GA) associated with rapid progression and a diffuse-trickling appearance on fundus autofluorescence (FAF).<h4>Methods</h4>Thirty-six patients (60 eyes; 72.2% women; mean age, 69.4 ± 10.7 years) with this distinct phenotype were examined by simultaneous confocal scanning laser ophthalmoscopy (cSLO) and spectral-domain optical coherence tomography (SD-OCT) imaging. Images were qualitatively and ...[more]

PMID: 21310912

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Project description:IMPORTANCE Evidence is mounting that achromatopsia is a progressive retinal degeneration, and treatments for this condition are on the horizon. OBJECTIVES To categorize achromatopsia into clinically identifiable stages using spectral-domain optical coherence tomography and to describe fundus autofluorescence imaging in this condition. DESIGN, SETTING, AND PARTICIPANTS A prospective observational study was performed between 2010 and 2012 at the Edward S. Harkness Eye Institute, New York-Presbyterian Hospital. Participants included 17 patients (aged 10-62 years) with full-field electroretinography-confirmed achromatopsia. MAIN OUTCOMES AND MEASURES Spectral-domain optical coherence tomography features and staging system, fundus autofluorescence and near-infrared reflectance features and their correlation to optical coherence tomography, and genetic mutations served as the outcomes and measures. RESULTS Achromatopsia was categorized into 5 stages on spectral-domain optical coherence tomography: stage 1 (2 patients [12%]), intact outer retina; stage 2 (2 patients [12%]), inner segment ellipsoid line disruption; stage 3 (5 patients [29%]), presence of an optically empty space; stage 4 (5 patients [29%]), optically empty space with partial retinal pigment epithelium disruption; and stage 5 (3 patients [18%]), complete retinal pigment epithelium disruption and/or loss of the outer nuclear layer. Stage 1 patients showed isolated hyperreflectivity of the external limiting membrane in the fovea, and the external limiting membrane was hyperreflective above each optically empty space. On near infrared reflectance imaging, the fovea was normal, hyporeflective, or showed both hyporeflective and hyperreflective features. All patients demonstrated autofluorescence abnormalities in the fovea and/or parafovea: 9 participants (53%) had reduced or absent autofluorescence surrounded by increased autofluorescence, 4 individuals (24%) showed only reduced or absent autofluorescence, 3 patients (18%) displayed only increased autofluorescence, and 1 individual (6%) exhibited decreased macular pigment contrast. Inner segment ellipsoid line loss generally correlated with the area of reduced autofluorescence, but hyperautofluorescence extended into this region in 2 patients (12%). Bilateral coloboma-like atrophic macular lesions were observed in 1 patient (6%). Five novel mutations were identified (4 in the CNGA3 gene and 1 in the CNGB3 gene). CONCLUSIONS AND RELEVANCE Achromatopsia often demonstrates hyperautofluorescence suggestive of progressive retinal degeneration. The proposed staging system facilitates classification of the disease into different phases of progression and may have therapeutic implications.

Project description:ImportanceThe identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans.ObjectiveTo develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan.Design, setting, and participantsThis retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023.ExposureA position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3).Main outcomes and measuresPrediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.ResultsThe study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3).Conclusions and relevanceThe fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.

Dataset Information

Fundus autofluorescence and spectral-domain optical coherence tomography characteristics in a rapidly progressing form of geographic atrophy.

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Fundus autofluorescence and spectral-domain optical coherence tomography characteristics in a rapidly progressing form of geographic atrophy.

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