Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration

Abstract

Purpose To probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain–optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings. Methods SD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure. Results In a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25–2.96] ( R 2 = 0.390) using random forest regression and 2.66 injections/year [2.31–3.01] ( R 2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models. Conclusions The proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings. Translational Relevance Prediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Evaluation of Multiple Machine Learning Models for Predicting Number of Anti-VEGF Injections in the Comparison of AMD Treatment Trials (CATT)

“…Using features extracted from real-world OCT scans of 96 nAMD patients treated with PRN or T&E protocols, Pfau et al 14 trained several machine learning models (LASSO, principal component, random forest, NGBoost), to predict the total number of injections, as well as to predict low (≤4 injections) and high (≥10 injections) treatment demand in one year. The random forest model yielded the greatest R 2 of 0.39 from nested cross-validation.…”

Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence

“…Yet, as imaging technology becomes more sophisticated, the discrepancy between image details and clinical interpretation is growing ( 6 ). There, AI becomes a useful tool as it has been applied to retinal OCT imaging to quantify fluid, ( 22 , 37 ) provide prognosis, ( 38 , 39 ) and predict treatment requirements in nAMD patients undergoing an anti-VEGF PRN regimen, ( 40 , 41 ) and more recently treatment demands in real-world cohorts ( 42 , 43 ).…”

“…We found the LCMM-based modeling to be a promising step toward the discovery of visual response subgroups and to offer a more comprehensive evaluation of the visual response. From the methodological perspective, this study follows the approach explored in previous works (38,(41)(42)(43), where the retina was first characterized with a set of quantitative biomarkers, followed by machine learning to build the predictive model. The results of predicting the treatment requirements (AUC of 0.71) fell slightly below the performance reported there, but within the confidence interval, where in Gallardo et al (43) they obtained an AUC of 0.79, and in Bogunovic et al (41) an AUC of 0.77 for predicting high-demand patients.…”

Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review

“…Using a random forest regression model, prediction of future anti-VEGF frequency was observed with an accuracy of 2.6 mean injections per year and 2.66 injections per year using an NGBoost model. RPE-drusen complex thickness in the central fovea was an important predictor across both models [ 111 ]. Likewise, SSG-NET, a sensitive structure guided network was used to predict short-term anti-VEGF requirements from 4944 OCT scans from nAMD patients.…”