Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach

Abstract

Purpose: The purpose of this study was to predict low and high anti-VEGF injection requirements during a pro re nata (PRN) treatment, based on sets of optical coherence tomography (OCT) images acquired during the initiation phase in neovascular AMD.

Methods: Two-year clinical trial data of subjects receiving PRN ranibizumab according to protocol specified criteria in the HARBOR study after three initial monthly injections were included. OCT images were analyzed at baseline, month 1, and month 2. Quantitative spatio-temporal features computed from automated segmentation of retinal layers and fluid-filled regions were used to describe the macular microstructure. In addition, best-corrected visual acuity and demographic characteristics were included. Patients were grouped into low and high treatment categories based on first and third quartile, respectively. Random forest classification was used to learn and predict treatment categories and was evaluated with cross-validation.

Results: Of 317 evaluable subjects, 71 patients presented low (≤5), 176 medium, and 70 high (≥16) injection requirements during the PRN maintenance phase from month 3 to month 23. Classification of low and high treatment requirement subgroups demonstrated an area under the receiver operating characteristic curve of 0.7 and 0.77, respectively. The most relevant feature for prediction was subretinal fluid volume in the central 3 mm, with the highest predictive values at month 2.

Conclusions: We proposed and evaluated a machine learning methodology to predict anti-VEGF treatment needs from OCT scans taken during treatment initiation. The results of this pilot study are an important step toward image-guided prediction of treatment intervals in the management of neovascular AMD.