Comparison of Fundus Autofluorescence Versus Optical Coherence Tomography-based Evaluation of the Therapeutic Response to Pegcetacoplan in Geographic Atrophy

[Complement inhibition treatment for geographic atrophy (GA): functional and morphological efficacy and relevant biomarkers in clinical practice]

The approval of complement inhibitory therapeutic agents for the treatment of geographic atrophy (GA) has highlighted the need for reliable and reproducible measurement of disease progression and therapeutic efficacy. Due to its availability and imaging characteristics optical coherence tomography (OCT) is the method of choice. Using OCT analysis based on artificial intelligence (AI), the therapeutic efficacy of pegcetacoplan was demonstrated at the levels of both the retinal pigment epithelium (RPE) and photoreceptors (PR). Cloud-based solutions that enable monitoring of GA are already available.

Correlation Between Blue Fundus Autofluorescence and SD-OCT Measurements of Geographic Atrophy in Dry Age-Related Macular Degeneration

PURPOSE

To compare fundus autofluorescence (FAF) and spectral domain optical coherence tomography (OCT) measurements of geographic atrophy (GA) area and to analyze lesion area changes measured by spectral domain OCT in GATHER1.

DESIGN

An assessment reliability analysis using prospective, randomized, double-masked phase 2/3 clinical trial data.

METHODS

GATHER1 examined the efficacy and safety of avacincaptad pegol (ACP) for GA treatment. A post hoc analysis was performed to identify correlations between FAF- and OCT-based measurements of GA. GA area was measured on blue-light FAF images using semiautomatic segmentation software with support from OCT and near-infrared imaging. Machine-learning enhanced, multilayer segmentation of OCT scans were reviewed by human readers, and segmentation errors were corrected as needed. GA area was defined as total RPE loss on cross-sectional B scans. Time points included Months 0, 6, 12, and 18. Additionally, OCT-based GA-area changes between ACP and sham were analyzed.

RESULTS

There was a strong correlation (r = 0.93) between FAF and OCT GA area measurements that persisted through 18 months. Mean (SD) differences between OCT and FAF GA measurements were negligible: 0.11 mm2 (1.42) at Month 0, 0.03 mm2 (1.62) at Month 6, -0.17 mm2 (1.81) at Month 12, and -0.07 mm2 (1.78) at Month 18. OCT assessments of GA growth revealed a 30% and 27% reduction at Months 12 and 18, respectively, between ACP and sham, replicating FAF measurements from GATHER1.

CONCLUSIONS

The strong correlation between blue FAF and OCT measurements of GA area supports OCT as a reliable method to measure GA lesion area in clinical trials.

AI-based support for optical coherence tomography in age-related macular degeneration

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers

The imagistic evaluation of non-neovascular age-related macular degeneration (AMD) is crucial for diagnosis, monitoring progression, and guiding management of the disease. Dry AMD, characterized primarily by the presence of drusen and retinal pigment epithelium atrophy, requires detailed visualization of the retinal structure to assess its severity and progression. Several imaging modalities are pivotal in the evaluation of non-neovascular AMD, including optical coherence tomography, fundus autofluorescence, or color fundus photography. In the context of emerging therapies for geographic atrophy, like pegcetacoplan, it is critical to establish the baseline status of the disease, monitor the development and expansion of geographic atrophy, and to evaluate the retina's response to potential treatments in clinical trials. The present review, while initially providing a comprehensive description of the pathophysiology involved in AMD, aims to offer an overview of the imaging modalities employed in the evaluation of non-neovascular AMD. Special emphasis is placed on the assessment of progression biomarkers as discerned through optical coherence tomography. As the landscape of AMD treatment continues to evolve, advanced imaging techniques will remain at the forefront, enabling clinicians to offer the most effective and tailored treatments to their patients.

Deep Multimodal Fusion of Data With Heterogeneous Dimensionality via Projective Networks

The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and classification using deep learning-based methods. However, current segmentation methods are limited to fusion of modalities with the same dimensionality (e.g., 3D + 3D, 2D + 2D), which is not always possible, and the fusion strategies implemented by classification methods are incompatible with localization tasks. In this work, we propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e.g., 3D + 2D) that is compatible with localization tasks. The proposed framework extracts the features of the different modalities and projects them into the common feature subspace. The projected features are then fused and further processed to obtain the final prediction. The framework was validated on the following tasks: segmentation of geographic atrophy (GA), a late-stage manifestation of age-related macular degeneration, and segmentation of retinal blood vessels (RBV) in multimodal retinal imaging. Our results show that the proposed method outperforms the state-of-the-art monomodal methods on GA and RBV segmentation by up to 3.10% and 4.64% Dice, respectively.

Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Effective biomarkers are required for assessing the progression of age-related macular degeneration (AMD), a prevalent and progressive eye disease. This paper presents a deep learning-based automated algorithm, applicable to both swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) scans, for measuring outer retinal layer (ORL) thickness as a surrogate biomarker for outer retinal degeneration, e.g., photoreceptor disruption, to assess AMD progression. The algorithm was developed based on a modified TransUNet model with clinically annotated retinal features manifested in the progression of AMD. The algorithm demonstrates a high accuracy with an intersection of union (IoU) of 0.9698 in the testing dataset for segmenting ORL using both SS-OCT and SD-OCT datasets. The robustness and applicability of the algorithm are indicated by strong correlation (r = 0.9551, P < 0.0001 in the central-fovea 3 mm-circle, and r = 0.9442, P < 0.0001 in the 5 mm-circle) and agreement (the mean bias = 0.5440 um in the 3-mm circle, and 1.392 um in the 5-mm circle) of the ORL thickness measurements between SS-OCT and SD-OCT scans. Comparative analysis reveals significant differences (P < 0.0001) in ORL thickness among 80 normal eyes, 30 intermediate AMD eyes with reticular pseudodrusen, 49 intermediate AMD eyes with drusen, and 40 late AMD eyes with geographic atrophy, highlighting its potential as an independent biomarker for predicting AMD progression. The findings provide valuable insights into the ORL alterations associated with different stages of AMD and emphasize the potential of ORL thickness as a sensitive indicator of AMD severity and progression.

Interreader Agreement and Longitudinal Progression of Incomplete/Complete Retinal Pigment Epithelium and Outer Retinal Atrophy in Age-Related Macular Degeneration

OBJECTIVE

To analyze the ability to evaluate changes over time of individual lesions of incomplete or complete retinal pigment epithelium (RPE) and outer retinal atrophy (iRORA and cRORA, respectively) in patients with intermediate age-related macular degeneration (iAMD).

DESIGN

OCT images from patients enrolled in Proxima B clinical trial (NCT02399072) were utilized.

PARTICIPANTS

Patients enrolled in the Proxima B clinical trial, from the cohort with geographic atrophy (GA) in 1 eye and iAMD in the other eye at baseline, were included.

METHODS

Junior and senior readers analyzed OCT images for the qualitative presence of 9 distinct early atrophic features (presence of zone of choroidal hypertransmission, attenuation and/or disruption of RPE, disruption of ellipsoid zone [EZ] and external limiting membrane [ELM], outer nuclear layer [ONL] thinning, outer plexiform layer [OPL]/inner nuclear layer [INL] subsidence, and hyporeflective wedge-shaped band). If deemed "present," 7 features were quantified with a predefined tolerance level of 50 μm (diameter for the zone of choroidal hypertransmission, zone of attenuation and/or disruption of the RPE, outer retinal thickness left/right vertical diameter, outer retinal thickness thinnest vertical diameter, annotation of EZ, and ELM disruption).

MAIN OUTCOME MEASURES

Interreader agreements for qualitative assessments (κ-type statistics) and quantitative measurements (Bland-Altman statistics) were assessed. Progression of the lesion features over time was described.

RESULTS

Moderate agreement was found for presence of choroidal hypertransmission (κ = 0.54), followed by ELM disruption (κ = 0.58), OPL/INL subsidence (κ = 0.46), and a hyporeflective wedge-shaped band (κ = 0.47). Quantification measurements showed that choroidal hypertransmission had the highest agreement, whereas RPE attenuation/disruption had the lowest agreement. Longitudinal adjudicated changes for quantitative measurements of lesion progression showed that choroidal hypertransmission and ELM disruption showed significant progression, whereas EZ disruption and RPE attenuation/disruption did not.

CONCLUSIONS

The ability to evaluate changes over time for specific features of iRORA and cRORA was explored. The most robust biomarker was found to be choroidal hypertransmission, followed by ELM disruption and the qualitative markers of OPL/INL subsidence, as well as a wedge-shaped band. Disease progression over time could be assessed by some, but not all, spectral-domain OCT features that were explored.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5)

Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.

[Monitoring of the progression of geographic atrophy with optical coherence tomography]

With the prospect of available therapy for geographic atrophy in the near future and consequently increasing patient numbers, appropriate management strategies for the clinical practice are needed. Optical coherence tomography (OCT) as well as automated OCT analysis using artificial intelligence algorithms provide optimal conditions for assessing disease activity as well as the treatment response for geographic atrophy through a rapid, precise and resource-efficient evaluation.

Progression Dynamics of Early versus Later Stage Atrophic Lesions in Nonneovascular Age-Related Macular Degeneration Using Quantitative OCT Biomarker Segmentation

PURPOSE

To investigate the progression of geographic atrophy secondary to nonneovascular age-related macular degeneration in early and later stage lesions using artificial intelligence-based precision tools.

DESIGN

Retrospective analysis of an observational cohort study.

SUBJECTS

Seventy-four eyes of 49 patients with ≥ 1 complete retinal pigment epithelial and outer retinal atrophy (cRORA) lesion secondary to age-related macular degeneration were included. Patients were divided between recently developed cRORA and lesions with advanced disease status.

METHODS

Patients were prospectively imaged by spectral-domain OCT volume scans. The study period encompassed 18 months with scheduled visits every 6 months. Growth rates of recent cRORA-converted lesions were compared with lesions in an advanced disease status using mixed effect models.

MAIN OUTCOME MEASURES

The progression of retinal pigment epithelial loss (RPEL) was considered the primary end point. Secondary end points consisted of external limiting membrane disruption and ellipsoid zone loss. These pathognomonic imaging biomarkers were quantified using validated deep-learning algorithms. Further, the ellipsoid zone/RPEL ratio was analyzed in both study cohorts.

RESULTS

Mean (95% confidence interval [CI]) square root progression of recently converted lesions was 79.68 (95% CI, -77.14 to 236.49), 68.22 (95% CI, -101.21 to 237.65), and 84.825 (95% CI, -124.82 to 294.47) mm/half year for RPEL, external limiting membrane loss, and ellipsoid zone loss respectively. Mean square root progression of advanced lesions was 131.74 (95% CI, -22.57 to 286.05), 129.96 (95% CI, -36.67 to 296.59), and 116.84 (95% CI, -90.56 to 324.3) mm/half year for RPEL, external limiting membrane loss, and ellipsoid zone loss, respectively. RPEL (P = 0.038) and external limiting membrane disruption (P = 0.026) progression showed significant differences between the 2 study cohorts. Further recent converters had significantly (P < 0.001) higher ellipsoid zone/RPEL ratios at all time points compared with patients in an advanced disease status (1.71 95% CI, 1.12-2.28 vs. 1.14; 95% CI, 0.56-1.71).

CONCLUSION

Early cRORA lesions have slower growth rates in comparison to atrophic lesions in advanced disease stages. Differences in growth dynamics may play a crucial role in understanding the pathophysiology of nonneovascular age-related macular degeneration and for the interpretation of clinical trials in geographic atrophy. Individual disease monitoring using artificial intelligence-based quantification paves the way toward optimized geographic atrophy management.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Complement Inhibitors for Advanced Dry Age-Related Macular Degeneration (Geographic Atrophy): Some Light at the End of the Tunnel?

Geographic atrophy (GA) affects around 5 million individuals worldwide. Genome-wide, histopathologic, in vitro and animal studies have implicated the activation of the complement system and chronic local inflammation in the pathogenesis of GA. Recently, clinical trials have demonstrated that an intravitreal injection of pegcetacoplan, a C3 inhibitor, and avacincaptad pegol, a C5 inhibitor, both statistically significantly reduce the growth of GA up to 20% in a dose-dependent fashion. Furthermore, the protective effect of both pegcetacoplan and avacincaptad appear to increase with time. However, despite these anatomic outcomes, visual function has not improved as these drugs appear to only slow down the degenerative process. Unexpected adverse events included conversion to exudative NV-AMD with both drugs. Occlusive retinal vasculitis and anterior ischemic optic neuropathy have been reported in pegcetacoplan-treated eyes.

Identifying geographic atrophy

PURPOSE OF REVIEW

Age-related macular degeneration (AMD) is one of the leading causes of blindness and can progress to geographic atrophy (GA) in late stages of disease. This review article highlights recent literature which assists in the accurate and timely identification of GA, and monitoring of GA progression.

RECENT FINDINGS

Technology for diagnosing and monitoring GA has made significant advances in recent years, particularly regarding the use of optical coherence tomography (OCT). Identification of imaging features which may herald the development of GA or its progression is critical. Deep learning applications for OCT in AMD have shown promising growth over the past several years, but more prospective studies are needed to demonstrate generalizability and clinical utility.

SUMMARY

Identification of GA and of risk factors for GA development or progression is essential when counseling AMD patients and discussing prognosis. With new therapies on the horizon for the treatment of GA, identification of risk factors for the development and progression of GA will become critical in determining the patients who would be appropriate candidates for new targeted therapies.