Pinedo-Diaz German, Lorenz Birgit, Künzel Sandrine H, Thiele Sarah, Ortega-Cisneros Susana, Corrochano Eduardo Bayro, Holz Frank G, Effland Alexander
Center for Research and Advanced Studies, Cinvestav, Zapopan, Mexico.
Dept of Ophthalmology, University Hospital, Bonn, Germany.
Invest Ophthalmol Vis Sci. 2025 Jan 2;66(1):5. doi: 10.1167/iovs.66.1.5.
To quantify outer retina structural changes and define novel biomarkers of inherited retinal degeneration associated with biallelic mutations in RPE65 (RPE65-IRD) in patients before and after subretinal gene augmentation therapy with voretigene neparvovec (Luxturna).
Application of advanced deep learning for automated retinal layer segmentation, specifically tailored for RPE65-IRD. Quantification of five novel biomarkers for the ellipsoid zone (EZ): thickness, granularity, reflectivity, and intensity. Estimation of the EZarea in single and volume scans was performed with optimized segmentation boundaries. The control group was age similar and without significant refractive error. Spherical equivalent refraction and ocular length were evaluated in all patients.
We observed significant differences in the structural analysis of EZ biomarkers in 22 patients with RPE65-IRD compared with 94 healthy controls. Relative EZ intensities were already reduced in pediatric eyes. Reductions of EZ local granularity and EZ thickness were only significant in adult eyes. Distances of the outer plexiform layer, external limiting membrane, and Bruch's membrane to EZ were reduced at all ages. EZ diameter and area were better preserved in pediatric eyes undergoing therapy with voretigene neparvovec and in patients with a milder phenotype.
Automated quantitative analysis of biomarkers within EZ visualizes distinct structural differences in the outer retina of patients including treatment-related effects. The automated approach using deep learning strategies allows big data analysis for distinct forms of inherited retinal degeneration. Limitations include a small dataset and potential effects on OCT scans from myopia at least -5 diopters, the latter considered nonsignificant for outer retinal layers.
量化视网膜外层结构变化，并确定在接受维替泊汀（Luxturna）视网膜下基因增强治疗前后，与RPE65双等位基因突变相关的遗传性视网膜变性（RPE65-IRD）患者的新型生物标志物。
应用先进的深度学习进行视网膜层自动分割，特别针对RPE65-IRD进行定制。量化椭圆体带（EZ）的五个新型生物标志物：厚度、颗粒度、反射率和强度。使用优化的分割边界对单幅扫描和容积扫描中的EZ面积进行估计。对照组年龄相仿且无明显屈光不正。对所有患者评估等效球镜度和眼轴长度。
与94名健康对照相比，我们观察到22名RPE65-IRD患者的EZ生物标志物结构分析存在显著差异。儿童眼中相对EZ强度已经降低。EZ局部颗粒度和EZ厚度的降低仅在成人眼中显著。在所有年龄段，外丛状层、外界膜和布鲁赫膜到EZ的距离均减小。接受维替泊汀治疗的儿童眼和表型较轻的患者中，EZ直径和面积保存得更好。
对EZ内生物标志物进行自动定量分析可显示患者视网膜外层的明显结构差异，包括与治疗相关的效应。使用深度学习策略的自动化方法允许对不同形式的遗传性视网膜变性进行大数据分析。局限性包括数据集较小以及至少-5屈光度的近视对光学相干断层扫描（OCT）扫描的潜在影响，后者对外层视网膜层影响不大。
