Shifting the GA paradigm

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Researchers have been delving into geographic atrophy (GA) to increase the knowledge base of end-stage age-related macular degeneration (AMD). The focus has been on early intervention to identify and prevent progression of GA.

The introduction of artificial intelligence (AI) into the investigative process is giving researchers an edge by simplifying their efforts in imaging analysis and providing more precise retinal details in patients with GA secondary to AMD.

FILLY study

Ursula Schmidt-Erfurth, MD, reported the results of a post hoc analysis of an optical coherence tomography (OCT)–based AI study at the 2024 American Society of Retina Specialists Annual Meeting in Stockholm, Sweden, that prospectively analyzed the natural progression of GA in the FILLY randomized clinical trial (NCT02503332) in participants treated with pegcetacoplan therapy (Syfovre; Apellis Pharmaceuticals). She is a professor and the chair of the Department of Ophthalmology at the Medical University of Vienna in Austria.

In that study, ophthalmologists sought to predict the yearly growth rate of GA and to select the potentially faster-growing lesions from 2 eyes based on fundus autofluorescence (FAF), near-infrared reflectance (NIR), and OCT. A deep learning algorithm predicted progression solely on the baseline OCT (Spectralis; Heidelberg Engineering).¹

A total of 134 eyes of 134 patients from the phase 2 clinical trial were included, and 2880 gradings were performed by 4 ophthalmologists. The main outcomes were the accuracy, weighted κ, and concordance index (c-index).

Schmidt-Erfurth noted, “Human experts reached accuracy values of 0.37, 0.43, and 0.41, and values of 0.06, 0.16, and 0.18 on FAF, NIR+OCT and FAF+NIR+OCT, respectively. A pairwise comparison task showed that human experts achieved c-indices of 0.62, 0.59, and 0.60.” The automated AI-based analysis reached an accuracy of 0.48, a κ value of 0.23 on the first task, and a c-index of 0.69 on the second task using only OCT imaging.

She pointed out that although the human gradings have improved with the availability of OCT, AI performed in an automated manner that is proving to be superior to and faster than analyses performed by ophthalmologists. The advantages of AI are that the technology is fully automated and provides reliable analysis of routine OCT as well as being accessible at any place and any time through the cloud.

OAKS and DERBY trials

Another recently published report1 also underscored the contribution of AI technology to increasing the knowledge about GA.

Schmidt-Erfurth was lead author of a post hoc OCT-based AI analysis performed to identify changes in the mean area of retinal pigment epithelial (RPE) loss and ellipsoidal zone (EZ) loss over time. The results showed that OCT-based AI analysis objectively identified and quantified degeneration of the photoreceptor and RPE in patients with GA secondary to AMD.

The investigators wanted to quantify the morphologic changes of the photoreceptors and RPE layers in patients with GA treated with pegcetacoplan therapy. Patients with GA due to AMD had been participants in 2 prospective randomized phase 3 clinical trials (OAKS, NCT03525613; DERBY, NCT03525600). Spectral-domain OCT images were analyzed over 24 months for changes in the mean area of RPE loss and EZ loss over time in the pooled sham arms and the monthly (PM)/every other month (PEOM) treatment arms.

Analysis of results

A total of 897 eyes of 897 patients were included. At 24 months, the analysis showed a therapeutic reduction of RPE loss growth by 22% and 20% in the OAKS trial and 27% and 21% in DERBY for PM/PEOM compared with the sham arm, respectively. The authors also reported that the reduction on the EZ level was significantly higher with 53% and 46% in the OAKS trial and 47% and 46% in the DERBY trial for PM/PEOM compared with sham at 24 months.

"The therapeutic benefit for RPE loss growth increased with larger EZ-RPE difference quartiles from 21.9%, 23.1%, 23.9% to 33.6% for PM vs sham (P < .01 for all comparisons ) and from 13.6% (P = .11), 23.8%, 23.8% to 20.0% for PEOM vs sham (P < .01 for all comparisons) in quartiles 1, 2, 3, and 4, respectively, at 24 months," they said. "Regarding maintenance of the EZ layer, the therapeutic reduction of loss increased from 14.8% (P = 0.09), 33.3%, 46.6% to 77.8% (P < .0001) between PM and sham and from 15.9% (P =.08), 33.8%, 52.0% to 64.9% (P < .0001) between PEOM and sham for quartiles 1 to 4 at 24 months.”

Based on their findings, the authors concluded that OCT-based AI analysis objectively identifies and quantifies PR and RPE degeneration in GA.

Schmidt-Erfurth and colleagues also pointed out that AI-based clinical tools will become widely available via cloud-based technology. "Our research effort undertaken in this context may add another step for envisioning clinical end points and medical devices that eventually will benefit providers, health care systems, and patients.”

Reference:

1. Schmidt-Erfurth U, Mai J, Reiter GS, et al. Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT. Ophthalmology. Published online August 14, 2024.doi:10.1016/j.ophtha.2024.08.017