Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration

Abstract

Purpose: To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD).

Design: Post hoc analysis of a randomized, prospective clinical trial.

Participants: Data of 614 evaluable patients receiving intravitreal ranibizumab monthly or pro re nata according to protocol-specified criteria in the HARBOR trial.

Methods: Monthly spectral-domain (SD) OCT volume scans were processed by validated, fully automated computational image analysis. This system performs spatially resolved 3-dimensional segmentation of retinal layers, intraretinal cystoid fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachments (PED). The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning. This approach was also used to correlate OCT morphology to BCVA at baseline (structure-function correlation).

Main outcome measures: Accuracy (R²) of the prediction models; ranking of input variables.

Results: Computational image analysis enabled fully automated quantitative characterization of neovascular lesions in a large-scale clinical SD-OCT data set. At baseline, OCT features and BCVA were correlated with R² = 0.21. The most relevant biomarker for BCVA was the horizontal extension of IRF in the foveal region, whereas SRF and PED ranked low. In predicting functional outcomes, the model's accuracy increased in a linear fashion with each month. If only the baseline visit was considered, the accuracy was R² = 0.34. At month 3, final visual acuity outcomes could be predicted with an accuracy of R² = 0.70. The strongest predictive factor for functional outcomes at 1 year was consistently the individual BCVA level during the initiation phase.

Conclusions: In this large-scale study based on a wide spectrum of morphologic and functional features, baseline BCVA correlated modestly with baseline SD-OCT, whereas functional outcomes were determined by BCVA levels during the initiation phase with a minor influence of fluid-related features. This finding suggests a re-evaluation of current diagnostic imaging features and a search for novel imaging approaches, where machine learning is a promising approach.