Reliability of retinal pathology quantification in age-related macular degeneration: Implications for clinical trials and machine learning applications

P. Muller, B. Liefers, T. Treis, F. Gomes Rodrigues, A. Olvera-Barrios, B. Paul, N. Dhingra, A. Lotery, C. Bailey, P. Taylor, C. Sánchez and A. Tufail

medrxiv 2020.

DOI

Purpose: To investigate the inter-reader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting. Methods: In this cross-sectional case series, spectral domain optical coherence tomography (OCT, Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular AMD (56 treatment-naive, 56 after three anti-vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland-Altman plots, coefficients of repeatability (CR), coefficients of variation (CV), and intraclass correlation coefficients (ICC) were assessed. Results: Loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and Drusen were the most abundant features in our cohort. The features subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest inter-reader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features. Conclusions: The inter-reader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages. This might give opportunities to a more time- and cost-effective patient assessment and improved decision-making as well as have implications for clinical trials and training machine learning algorithms.