Prognostic Value of Shape-Descriptive Factors for the Progression of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Pfau, Maximilian; Lindner, Moritz; Goerdt, Lukas; Thiele, Sarah; Nadal, Jennifer; Schmid, Matthias; Schmitz-Valckenberg, Steffen; Sadda, Srinivas R; Holz, Frank G.; Fleckenstein, Monika

doi:10.1097/iae.0000000000002206

Cited by 55 publications

(63 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 Some authors found advantageous the square-root transformation of the RPE-atrophy rates because it may reduce the effect of baseline RPEatrophy area. 4,[50][51][52] In our study, we repeated all the analyses with the square-root-transformed RPEatrophy progression rate as the dependent variable, and we found similar results to nontransformed data.…”

Section: Discussionsupporting

confidence: 54%

Factors Influencing Retinal Pigment Epithelium-Atrophy Progression Rate in Stargardt Disease

Cicinelli

Rabiolo

Brambati

et al. 2020

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

To evaluate demographic, clinical, imaging, and genetic factors associated with retinal pigment epithelium enlargement in Stargardt disease (STGD1) and to measure the agreement between short-wavelength fundus autofluorescence (SW-FAF) and near-infrared fundus autofluorescence (NIR-FAF). Methods: Retrospective cohort study of patients with STGD1 with ≥2 gradable SW-FAF images. RPE-atrophy areas were measured on SW-FAF and NIR-FAF at each visit and regressed against time to obtain the rate of RPE-atrophy enlargement. Agreement between SW-FAF and NIR-FAF with regards to baseline atrophic areas and rates of enlargement was evaluated. Baseline factors predictive of faster SW-FAF RPE-atrophy enlargement were investigated with linear mixed models. Results: Fifty-four eyes of 28 patients (median age: 45 years; 13 males) were included. SW-FAF and NIR-FAF agreed well for slow rates of RPE-atrophy progression, but agreement decreased as the rate increased. Median (interquartile range [IQR]) rate of RPE-atrophy expansion was 0.18 (0.10-0.85) mm 2 /year on SW-FAF and 0.24 (0.08-0.33) mm 2 /year on NIR-FAF. Larger baseline RPE-atrophy area (estimate: 0.057 mm 2 /year, P < 0.001), worse visual acuity (0.305 mm 2 /year, P = 0.005), multifocal disease (0.401 mm 2 /year, P = 0.02), and SW-FAF pattern (0.534 mm 2 /year, P =0 .03) were associated with a faster rate of progression (predictive R 2 : 0.65). Conclusions: SW-FAF and NIR-FAF are not interchangeable in the evaluation of RPEatrophy enlargement, and both imaging modalities may be required for optimal detection of disease progression. A multivariable model based on baseline clinical and imaging information may identify patients at higher risk of fast disease progression. Translational Relevance: The knowledge of the agreement of different FAF modalities, the estimated rates of RPE-atrophy enlargement, and factors predictive of faster anatomic decay in STGD1 may allow tailored clinical management and better clinical trials design.

show abstract

Section: Discussionsupporting

confidence: 54%

Factors Influencing Retinal Pigment Epithelium-Atrophy Progression Rate in Stargardt Disease

Cicinelli

Rabiolo

Brambati

et al. 2020

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

show abstract

“…Patients were recruited in the context of the noninterventional prospective natural history study Directional Spread in Geographic Atrophy (DSGA, NCT02051998) at the Department of Ophthalmology at the University Hospital in Bonn, Germany. 19,20 Briefly, patients had to be at least 55 years of age and exhibit unifocal or multifocal GA, defined as any sharply demarcated round or oval-shaped lesion of at least 0.05 mm 2 in area, and have clearly reduced FAF in the context of AMDassociated changes (i.e., drusen and pigment abnormalities). 19 Exclusion criteria included the presence of other retinal diseases such as diabetic retinopathy or retinal dystrophy, as well as a history of laser photocoagulation or retinal surgery, any signs of active or regressed exudation (e.g., hemorrhages, exudates, fibrous scarring), as well as refractive errors >5.00 diopters spherical equivalent or >2.50 diopters astigmatism in the study eye.…”

Section: Methods Patientsmentioning

confidence: 99%

Light Sensitivity Within Areas of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Pfau

Emde

Dysli

et al. 2019

Invest. Ophthalmol. Vis. Sci.

Self Cite

View full text Add to dashboard Cite

PURPOSE. To investigate residual sensitivity within geographic atrophy (GA) secondary to agerelated macular degeneration. METHODS. Mesopic and dark-adapted (DA) cyan and red light sensitivity (Goldmann III) were investigated using fundus-controlled perimetry (microperimetry). Test points were placed within GA along an ''iso-hull'' with a distance of À0.6458 to the atrophy boundary. The falsepositive response rate was determined with suprathreshold stimuli to the optic disc (Heijl-Krakau method) and used to compute the expected sensitivity measurements for the assumption of absolute scotomata. The outermost visible retinal layer on spectral-domain optical coherence tomography at the location of each test point was determined. RESULTS. Thirty eyes of 36 patients (75.55 6 7.93 years; 19 female) from the prospective natural history study Directional Spread in Geographic Atrophy (NCT02051998), with a total of 1380 threshold determinations were analyzed. The measured sensitivities were significantly (P < 0.01) higher than the expected values for absolute scotomata (mean 6 standard error of þ6.92 6 0.86 dB for mesopic, þ2.57 6 0.56 dB for DA cyan, and þ4.93 6 0.74 dB for DA red testing). For mesopic testing and DA red testing, the presence of a residual outer nuclear layer had a significant effect on this discrepancy (P < 0.001). There was no effect of fixation stability or any other reliability index on this discrepancy. CONCLUSIONS. Measured sensitivities within the inner junctional zone of GA may not be purely explained by patient-specific false-positive response rates or other reliability indices. The marked influence of the outer retinal configuration on measured sensitivity may be indicative of residual cone function within GA at the inner junctional zone.

show abstract

“…Of the 27 publications found, 18 were dedicated to GA segmentation only (e.g. lesions or retinal layers that explain GA), 2 focused on the detection and classification of GA, 2 assessed overall GA progression (with one including segmentation as well), 3 assessed spatial GA progression (with 2 including segmentation), and finally 2 assessed visual function prediction in GA. No publications were found that discussed other aspects of automating GA, such as the automated extraction of hyperfluorescent areas, although some, such as Pfau et al, 43 did assess hyperfluorescent phenotypes in the modeling process. Sample sizes ranged from 16 to 59,812 images with the latter being a subset of images from the Age-Related Eye Disease Study (AREDS) dataset and used by Keenan et al (2019) 24 for the detection and classification of GA-related models.…”

Section: Resultsmentioning

confidence: 99%

“…Two publications discussed overall GA progression: Pfau et al and Liefers et al 43 , 44 Pfau and colleagues evaluated shape-descriptive factors on lesion progression and they quantified this using a linear mixed-effects model with a two-level random effect (i.e. eye- and patient-specific effects).…”

Section: Resultsmentioning

confidence: 99%

“…various hyperfluorescent patterns) as a feature in a cross-validated model, revealing low predictive value compared to shape-descriptive factors. 43 The potential role of hyperfluorescence in the manifestation and progression of GA has been assessed previously. 43 , 65 Simple predictive models were used, rather than AI algorithms, and thus the evaluation of hyperfluorescence association with GA progression is a neglected area of GA-AI research.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Arslan

Samarasinghe

Benke

et al. 2020

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

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.

show abstract

Prognostic Value of Shape-Descriptive Factors for the Progression of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Cited by 55 publications

References 67 publications

Factors Influencing Retinal Pigment Epithelium-Atrophy Progression Rate in Stargardt Disease

Factors Influencing Retinal Pigment Epithelium-Atrophy Progression Rate in Stargardt Disease

Light Sensitivity Within Areas of Geographic Atrophy Secondary to Age-Related Macular Degeneration

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

Contact Info

Product

Resources

About