Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification

Final Outcomes from the Randomized RECOVERY Trial of Aflibercept for Retinal Nonperfusion in Proliferative Diabetic Retinopathy

PURPOSE

Retinal nonperfusion (RNP) is an important biomarker for diabetic retinopathy (DR). Data suggest that consistent anti-VEGF pharmacotherapy can slow RNP development. The RECOVERY trial evaluated the impact of aflibercept (Eylea, Regeneron) on RNP among eyes with proliferative DR (PDR).

DESIGN

Prospective, randomized clinical trial with treatment crossover in the second year.

SUBJECTS

Eyes with PDR and RNP.

METHODS

At baseline, the subjects were randomized 1:1 to monthly (arm 1) or quarterly (arm 2) intravitreal 2 mg aflibercept. At the beginning of year 2, the treatment arms were crossed over so that the monthly-dosed subjects subsequently received quarterly dosing and the quarterly-dosed subjects subsequently received monthly dosing.

MAIN OUTCOME MEASURES

Change in total RNP area (mm²) through year 2. Secondary outcomes included Diabetic Retinopathy Severity Scale (DRSS) scores; best-corrected visual acuity; central subfield thickness; additional measures of RNP, including ischemic index (ISI); and adverse event incidence. Means and 95% confidence intervals were calculated.

RESULTS

Among all subjects, from baseline to year 2, the mean RNP increased from 235 mm² to 402 mm² (P < 0.0001), and the ISI increased from 25.8% to 50.4% (P < 0.0001). Increases in the mean RNP (P < 0.0001) and ISI (P < 0.0001) were also observed from year 1 to year 2. The mean total RNP increased from 264 mm² at baseline to 386 mm² (P < 0.0001) at year 2 in arm 1 and from 207 mm² at baseline to 421 mm² (P < 0.0001) at year 2 in arm 2 (P = 0.023, arm 1 vs. arm 2). Increases in the mean RNP for both treatment arms (P < 0.0001) were also specifically observed within year 2 (P = 0.32, arm 1 vs. arm 2). Compared with baseline, the DRSS scores at the end of year 2 improved in 82% (n = 27) of subjects and remained stable in 18% (n = 6), with no subjects experiencing worsening; at 2 years, the DRSS scores had improved by 2 or more steps in 65% (n = 11) and 81% (n = 13) of subjects in arms 1 and 2, respectively.

CONCLUSIONS

Through year 2 of the RECOVERY trial, both treatment arms experienced significant increases in RNP. Despite the expansion of the RNP area in nearly all subjects, 82% of subjects demonstrated an improvement in DRSS levels from baseline, with no subjects experiencing worsening in DRSS scores.

Predictive Assessment of Quantitative Ultra-Widefield Angiographic Features for Future Need for Anti-VEGF Therapy in Diabetic Eye Disease

The objective of this study was to identify biomarkers that predict a future need for anti-VEGF therapy in diabetic retinopathy (DR). Eyes with DR that underwent ultra-widefield angiography (UWFA) and had at least a 1 year follow-up were grouped based on future anti-VEGF treatment requirements: (1) not requiring treatment, (2) immediate treatment (within 3 months of UWFA), and (3) delayed treatment (after 3 months of UWFA). Quantitative UWFA features and clinical factors were evaluated. Random forest models were built to differentiate eyes requiring immediate and delayed treatment from the eyes not requiring treatment. A total of 173 eyes were included. The mean follow-up was 22 (range: 11–43) months. The macular leakage index, panretinal leakage index, presence of DME, and visual acuity were significantly different in eyes requiring immediate (n = 38) and delayed (n = 34) treatment compared to eyes not requiring treatment (n = 101). Random forest model differentiating eyes requiring immediate treatment from eyes not requiring treatment demonstrated an AUC of 0.91 ± 0.07. Quantitative angiographic features have potential as important predictive biomarkers of a future need for anti-VEGF therapy in DR and may serve to guide the frequency of a follow-up.

Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine

The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.