Distinct Genetic Risk Profile of the Rapidly Progressing Diffuse-Trickling Subtype of Geographic Atrophy in Age-Related Macular Degeneration (AMD)

Fleckenstein, Monika; Graßmann, Felix; Lindner, Moritz; Pfau, Maximilian; Czauderna, J; Strunz, Tobias; Strachwitz, C. von; Schmitz-Valckenberg, Steffen; Holz, Frank G.; Weber, Bernhard H. F.

doi:10.1167/iovs.15-18593

Cited by 24 publications

(24 citation statements)

References 39 publications

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“…6 7 GA progression was shown to be associated with perilesional phenotypes in fundus autofluorescence (FAF) imaging which exhibit to some degree a distinct genetic risk profile, especially with regard to the ARMS2 risk allele and the diffuse-trickling FAF pattern. 8 In the context of iAMD, other studies have also demonstrated an association of this genetic risk variant with reticular pseudodrusen (RPD), an ocular iAMD biomarker substantially impacting the risk for conversion into advanced disease stages. [9][10][11] However, only little is known about the prognostic relevance of structural iAMD phenotypes across different, that means from early to advanced, disease stages since most studies in early or iAMD exclude data once the study outcome, development of late-stage AMD, is reached.…”

Section: Introductionmentioning

confidence: 99%

Prognostic value of intermediate age-related macular degeneration phenotypes for geographic atrophy progression

et al. 2020

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BackgroundTo characterise early stages of geographic atrophy (GA) development in age-related macular degeneration (AMD) and to determine the prognostic value of structural precursor lesions in eyes with intermediate (i) AMD on the subsequent GA progression.MethodsStructural precursor lesions for atrophic areas (lesion size at least 0.5 mm² in fundus autofluorescence images) were retrospectively identified based on multimodal imaging and evaluated for association with the subsequent GA enlargement rates (square-root transformed, sqrt). A linear mixed-effects model was used to account for the hierarchical nature of the data with a Tukey post hoc test to assess the impact of the local precursor on the subsequent GA progression rate.ResultsA total of 39 eyes with GA of 34 patients with a mean age of 74.4±6.7 (±SD) years were included in this study. Five precursor lesions (phenotypes 1–5) preceding GA development were identified: large, sub-retinal pigment epithelial drusen (n=19), reticular pseudodrusen (RPD, n=10), refractile deposits (n=4), pigment epithelial detachment (n=4) and vitelliform lesions (n=2). Precursor lesions exhibited a significant association with the subsequent (sqrt) GA progression rates (p=0.0018) with RPD (phenotype 2) being associated with the fastest GA enlargement (2.29±0.52 (±SE) mm/year.ConclusionsThe results indicate the prognostic relevance of iAMD phenotyping for subsequent GA progression highlighting the role of structural AMD features across different AMD stages.

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Section: Introductionmentioning

confidence: 99%

Prognostic value of intermediate age-related macular degeneration phenotypes for geographic atrophy progression

et al. 2020

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“…a rapidly progressing autofluorescence phenotype, which was previously shown to exhibit a distinct genetic risk profile). 23 , 51 Their training accuracies ranged from 0.98 to 0.99, whereas validation accuracies ranged from 0.77 to 0.96. Keenan et al 24 generated three binary GA-related classification models using CFP and CNN: a GA detection, a central-GA detection, and a centrality detection model.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Arslan

Samarasinghe

Benke

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression). Methods The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression). Results Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89. Conclusions Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds. Translational Relevance AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.

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“…For example, choroidal insufficiency has been previously implicated in the pathogenesis of subphenotypes within the spectrum of AMD including ''reticular macular disease'' and ''diffuse-trickling geographic atrophy'' based on indocyanine green and fluorescein angiography findings, pronounced choroidal thinning, and cardiovascular comorbidity. [33][34][35][36][37] Limitations of this study need to be considered. While STGD1 and AMD patients exhibited phenotypic differences inside of RPE atrophy, these differences appeared to be not adequately quantified by the AFS area fraction.…”

Section: Discussionmentioning

confidence: 98%

“…To avoid potential confounding, patients exhibiting ''diffusetrickling geographic atrophy'' were not included, as choroidal insufficiency has been previously implicated in the pathogenesis. [33][34][35][36][37] Genetic testing was conducted at the Institute of Human Genetics, University of Regensburg (n ¼ 7), and at the Center for Human Genetics Bioscientia, Ingelheim (n ¼ 6). Analysis of all coding exons of the ABCA4 and PRPH2 genes was done by either direct chain-terminating dideoxynucleotide Sanger sequencing, a custom-designed GeneChip CustomSeq Resequencing Array (RetChip; Affymetrix, Santa Clara, CA, USA), or next-generation sequencing (Regensburg, n ¼ 5; Bioscientia, n ¼ 6).…”

Section: Patient Characterizationmentioning

confidence: 99%

Choroidal Flow Signal in Late-Onset Stargardt Disease and Age-Related Macular Degeneration: An OCT-Angiography Study

Müller

Pfau

Möller

et al. 2018

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. To investigate the choroidal blood flow in areas within and adjacent to retinal pigment epithelium (RPE) atrophy secondary to late-onset Stargardt disease (STGD1) and agerelated macular degeneration (AMD). METHODS.A total of 43 eyes (23 STGD1 and 20 AMD) of patients with RPE atrophy and 25 eyes of healthy controls without ocular pathology underwent multimodal imaging including optical coherence tomography angiography (OCT-A; PLEX Elite 9000 Swept-Source OCT). Using an exploratory approach, choriocapillaris and deeper choroid OCT-A slabs were evaluated in order to detect differences between STGD1 and AMD. The magnitude of absenceof-flow signal (AFS) was investigated in terms of area-fraction and size-frequency distribution. RESULTS.Qualitative and quantitative analysis of areas of RPE atrophy revealed more pronounced rarefaction of the choriocapillaris flow signal in STGD1 as compared to AMD (AFS area fraction: 33.15% 6 6.86% vs. 31.68% 6 8.39%; P ¼ 0.517), while outside RPE atrophy rarefaction was less pronounced in STGD1 (AFS area fraction: 17.41% 6 5.67% vs. 21.59% 6 6.90%; P < 0.001), to the level of nonsignificance compared to controls (13.27% 6 2.99%, P ¼ 0.368). Given this discrepancy, the ratio of the AFS area fraction within/outside of RPE atrophy could be used to differentiate between STGD1 and AMD with 65.0% sensitivity and 92.3% specificity. CONCLUSIONS.Using OCT-A, comparison of choroidal flow signal within and outside the area of RPE atrophy revealed distinct differences between STGD1 and AMD, potentially implicating a differential role of the choroid in the pathogenesis of RPE atrophy in these two diseases.

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Distinct Genetic Risk Profile of the Rapidly Progressing Diffuse-Trickling Subtype of Geographic Atrophy in Age-Related Macular Degeneration (AMD)

Cited by 24 publications

References 39 publications

Prognostic value of intermediate age-related macular degeneration phenotypes for geographic atrophy progression

Prognostic value of intermediate age-related macular degeneration phenotypes for geographic atrophy progression

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Choroidal Flow Signal in Late-Onset Stargardt Disease and Age-Related Macular Degeneration: An OCT-Angiography Study

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