Growth Modeling for Quantitative, Spatially Resolved Geographic Atrophy Lesion Kinetics

Geographic Atrophy Natural History Versus Treatment: Time to Fovea

BACKGROUND AND OBJECTIVE

The Food and Drug Administration recently approved treatments of geographic atrophy (GA). Our study aims to quantify the time for a lesion to reach the central fovea based on reduction of GA growth rates from therapeutics compared to the natural history.

PATIENTS AND METHODS

A previously published study calculates local border expansion rate of GA lesions at varying retinal eccentricities. In this study, we use these rates to model GA expansion toward the fovea and the effects of treatments that reduce growth in GA area by 15% to 45% on lesions of varying sizes with posterior margin 250, 500, 750, 1000, 1250, 1500, and 3000 µm from the fovea.

RESULTS

Lesions with an area 8 mm2 and posterior edge 500 µm from the fovea will reach the fovea in 5.08 years with no treatment, but the same lesions will reach the fovea in 5.85, 6.52, 7.36, and 8.46 years with a treatment that reduces growth in GA area by 15%, 25%, 35%, and 45%, respectively.

CONCLUSIONS

Distance of the posterior edge of the lesion was the primary factor in GA growth toward the fovea, and lesion size only minimally affects growth rates of GA. Based on the efficacy of current and future therapeutics and distance of GA to the fovea, our study provides the marginal time benefit of treatment to guide patients and clinicians, placing both the natural history of GA and the effects of current and future treatments into clinical context. [Ophthalmic Surg Lasers Imaging Retina 2024;55:576-585.].

Association Between Quantitative and Qualitative Imaging Biomarkers and Geographic Atrophy Growth Rate

PURPOSE

Investigate associations between geographic atrophy (GA) growth rate and multimodal imaging biomarkers and patient demographics in patients with advanced non-neovascular age-related macular degeneration (nnAMD).

DESIGN

Prospective cohort study.

METHODS

One hundred twenty-one eyes of 66 patients with advanced nnAMD with GA enrolled in the University of Colorado AMD Registry from August 2014 to June 2021, with follow-up through June 2023. Multimodal images were reviewed by two graders for imaging biomarkers at enrollment. GA growth rate and square-root transformed (SQRT) GA growth rate were measured between enrollment and final visit. Associations between the outcome SQRT GA growth rate and imaging biomarkers, baseline GA lesions characteristics, and patient demographics were evaluated.

RESULTS

Average GA growth rate was 1.430 mm2/year and SQRT GA growth rate was 0.268 mm/year over a mean of 3.7 years. SQRT GA growth rate was positively associated with patient age (P = .010) and female sex (0.035), and negatively associated with body mass index (0.041). After adjustment for these demographic factors, SQRT GA growth rate was positively associated with presence of non-exudative subretinal fluid (P < .001), non-exudative subretinal hyperreflective material (P = .037), and incomplete retinal pigment epithelium and outer retina atrophy (P = .022), and negatively associated with subfoveal choroidal thickness (P = .031) and presence of retinal pseudocysts (P = .030). Larger baseline GA size at enrollment was associated with faster GA growth rate (P = .002) but not SQRT GA growth rate.

CONCLUSIONS

Select patient demographic factors and basic clinically-relevant imaging biomarkers were associated with GA growth rate. These biomarkers may guide patient selection when considering treating GA patients with novel therapeutics.

Age-Related Macular Degeneration, a Mathematically Tractable Disease

A progression sequence for age-related macular degeneration onset may be determinable with consensus neuroanatomical nomenclature augmented by drusen biology and eye-tracked clinical imaging. This narrative review proposes to supplement the Early Treatment of Diabetic Retinopathy Study (sETDRS) grid with a ring to capture high rod densities. Published photoreceptor and retinal pigment epithelium (RPE) densities in flat mounted aged-normal donor eyes were recomputed for sETDRS rings including near-periphery rich in rods and cumulatively for circular fovea-centered regions. Literature was reviewed for tissue-level studies of aging outer retina, population-level epidemiology studies regionally assessing risk, vision studies regionally assessing rod-mediated dark adaptation (RMDA), and impact of atrophy on photopic visual acuity. The 3 mm-diameter xanthophyll-rich macula lutea is rod-dominant and loses rods in aging whereas cone and RPE numbers are relatively stable. Across layers, the largest aging effects are accumulation of lipids prominent in drusen, loss of choriocapillary coverage of Bruch's membrane, and loss of rods. Epidemiology shows maximal risk for drusen-related progression in the central subfield with only one third of this risk level in the inner ring. RMDA studies report greatest slowing at the perimeter of this high-risk area. Vision declines precipitously when the cone-rich central subfield is invaded by geographic atrophy. Lifelong sustenance of foveal cone vision within the macula lutea leads to vulnerability in late adulthood that especially impacts rods at its perimeter. Adherence to an sETDRS grid and outer retinal cell populations within it will help dissect mechanisms, prioritize research, and assist in selecting patients for emerging treatments.

Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning

PURPOSE

To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in spectral-domain OCT (SD-OCT) by automated deep learning assessment.

DESIGN

Retrospective analysis of a phase II clinical trial study evaluating pegcetacoplan in GA patients (FILLY, NCT02503332).

SUBJECTS

SD-OCT scans of 57 eyes with monthly treatment, 46 eyes with every-other-month (EOM) treatment, and 53 eyes with sham injection from baseline and 12-month follow-ups were included, in a total of 312 scans.

METHODS

Retinal pigment epithelium loss, photoreceptor (PR) integrity, and hyperreflective foci (HRF) were automatically segmented using validated deep learning algorithms. Local progression rate (LPR) was determined from a growth model measuring the local expansion of GA margins between baseline and 1 year. For each individual margin point, the eccentricity to the foveal center, the progression direction, mean PR thickness, and HRF concentration in the junctional zone were computed. Mean LPR in disease activity and treatment effect conditioned on these properties were estimated by spatial generalized additive mixed-effect models.

MAIN OUTCOME MEASURES

LPR of GA, PR thickness, and HRF concentration in μm.

RESULTS

A total of 31,527 local GA margin locations were analyzed. LPR was higher for areas with low eccentricity to the fovea, thinner PR layer thickness, or higher HRF concentration in the GA junctional zone. When controlling for topographic and structural risk factors, we report on average a significantly lower LPR by -28.0% (95% confidence interval [CI], -42.8 to -9.4; P = 0.0051) and -23.9% (95% CI, -40.2 to -3.0; P = 0.027) for monthly and EOM-treated eyes, respectively, compared with sham.

CONCLUSIONS

Assessing GA progression on a topographic level is essential to capture the pathognomonic heterogeneity in individual lesion growth and therapeutic response. Pegcetacoplan-treated eyes showed a significantly slower GA lesion progression rate compared with sham, and an even slower growth rate toward the fovea. This study may help to identify patient cohorts with faster progressing lesions, in which pegcetacoplan treatment would be particularly beneficial. Automated artificial intelligence-based tools will provide reliable guidance for the management of GA in clinical practice.

Comparing Accuracies of Length-Type Geographic Atrophy Growth Rate Metrics Using Atrophy-Front Growth Modeling

Purpose

To compare the accuracies of the previously proposed square-root-transformed and perimeter-adjusted metrics for estimating length-type geographic atrophy (GA) growth rates.

Design

Cross-sectional and simulation-based study.

Participants

Thirty-eight eyes with GA from 27 patients.

Methods

We used a previously developed atrophy-front growth model to provide analytical and numerical evaluations of the square-root-transformed and perimeter-adjusted growth rate metrics on simulated and semisimulated GA growth data.

Main Outcome Measures

Comparison of the accuracies of the square-root-transformed and perimeter-adjusted metrics on simulated and semisimulated GA growth data.

Results

Analytical and numerical evaluations showed that the accuracy of the perimeter-adjusted metric is affected minimally by baseline lesion area, focality, and circularity over a wide range of GA growth rates. Average absolute errors of the perimeter-adjusted metric were approximately 20 times lower than those of the square-root-transformed metrics, per evaluation on a semisimulated dataset with growth rate characteristics matching clinically observed data.

Conclusions

Length-type growth rates have an intuitive, biophysical interpretation that is independent of lesion geometry, which supports their use in clinical trials of GA therapeutics. Taken in the context of prior studies, our analyses suggest that length-type GA growth rates should be measured using the perimeter-adjusted metric, rather than square-root-transformed metrics.

Local Geographic Atrophy Growth Rates Not Influenced by Close Proximity to Non-Exudative Type 1 Macular Neovascularization

Purpose

The local growth rates of geographic atrophy (GA) adjacent to non-exudative type 1 macular neovascularization (MNV) were investigated to determine if MNV influenced GA growth.

Methods

Eyes with GA and non-exudative type 1 MNV were followed for at least 1 year. Both GA and the MNV were imaged and measured using swept-source optical coherence tomography angiography (SS-OCTA) scans. Pearson correlations were computed between local growth rates of GA, which were estimated using a biophysical GA growth model, and local distances-to-MNV. Corresponding P values for the null hypothesis of no Pearson correlation were computed using a Monte Carlo approach that adjusts for spatial autocorrelations.

Results

Nine eyes were included in this study. There were positive correlations (Pearson's r > 0) between distance-to-MNV and local GA growth in eight (89%) of the eyes; however, in all but one eye (11%), correlations were relatively weak and statistically nonsignificant after Bonferroni correction (corrected P > 0.05).

Conclusions

SS-OCTA imaging combined with GA growth modeling and spatial statistical analysis enabled quantitative assessment of correlations between local GA growth rates and local distances-to-MNV. Our results are not consistent with non-exudative type 1 MNV having a strong inhibitory effect on local GA growth rates.

Personalized Atrophy Risk Mapping in Age-Related Macular Degeneration

Purpose

To develop and validate an automatic retinal pigment epithelial and outer retinal atrophy (RORA) progression prediction model for nonexudative age-related macular degeneration (AMD) cases in optical coherence tomography (OCT) scans.

Methods

Longitudinal OCT data from 129 eyes/119 patients with RORA was collected and separated into training and testing groups. RORA was automatically segmented in all scans and additionally manually annotated in the test scans. OCT-based features such as layers thicknesses, mean reflectivity, and a drusen height map served as an input to the deep neural network. Based on the baseline OCT scan or the previous visit OCT, en face RORA predictions were calculated for future patient visits. The performance was quantified over time with the means of Dice scores and square root area errors.

Results

The average Dice score for segmentations at baseline was 0.85. When predicting progression from baseline OCTs, the Dice scores ranged from 0.73 to 0.80 for total RORA area and from 0.46 to 0.72 for RORA growth region. The square root area error ranged from 0.13 mm to 0.33 mm. By providing continuous time output, the model enabled creation of a patient-specific atrophy risk map.

Conclusions

We developed a machine learning method for RORA progression prediction, which provides continuous-time output. It was used to compute atrophy risk maps, which indicate time-to-RORA-conversion, a novel and clinically relevant way of representing disease progression.

Translational Relevance

Application of recent advances in artificial intelligence to predict patient-specific progression of atrophic AMD.