Radiomics-Based Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration With Pigment Epithelial Detachment

Abstract

Purpose: Machine learning models based on radiomic feature extraction from clinical imaging data provide effective and interpretable means for clinical decision making. This pilot study evaluated whether radiomics features in baseline optical coherence tomography (OCT) images of eyes with pigment epithelial detachment (PED) associated with neovascular age-related macular degeneration (nAMD) can predict treatment response to as-needed anti-vascular endothelial growth factor (VEGF) therapy.

Methods: Thirty-nine eyes of patients with PED undergoing anti-VEGF therapy were included. All eyes underwent a loading dose followed by as-needed therapy. OCT images at baseline, month 3, and month 6 were analyzed. Images were manually separated into non-responding, recurring, and responding eyes based on the presence or absence of subretinal fluid at month 6. PED radiomics features were then extracted from each image and images were classified as responding or recurring using a machine learning classifier applied to the radiomics features.

Results: Linear discriminant analysis classification of baseline features as responsive versus recurring resulted in classification performance of 64.0% (95% confidence interval [CI] = 0.63-0.65), area under the curve (AUC = 0.78, 95% CI = 0.72-0.82), sensitivity 0.79 (95% CI = 0.63-0.87), and specificity 0.58 (95% CI = 0.50-0.67). Further analysis of features in recurring eyes identified a significant shift toward non-responding mean feature values over 6 months.

Conclusions: Our results demonstrate the use of radiomics features as predictors for treatment response to as-needed anti-VEGF therapy. Our study demonstrates the potential for radiomics feature in clinical decision support for personalizing anti-VEGF therapy.

Translational relevance: The ability to use PED texture features to predict treatment response facilitates personalized clinical decision making.

Figure 1.

Radiomics classification pipeline. Retinal OCT images were obtained at baseline, month 3, and month 6 follow-up appointments from nonresponding, recurring, and responding eyes. Images were preprocessed and then segmented to select for PED pixels. A battery of 52 texture features were then computed using the PED pixels. A linear discriminate analysis classifier was used to predict image response to as-needed anti-VEGF therapy.

Figure 2.

Features distinguishing responding and recurring eyes. Features distinguishing responding and recurring eyes were identified based on lowest P value on a non-paired t-test. Left: Overlay image showing the most distinguishing feature, pixel entropy, for nonresponding, recurring, and responding eyes at baseline, month 3, and month 6. Right: Distribution of four features most distinguishing responding and recurring eyes separated into nonresponding, recurring, and responding feature values.

Figure 3.

Recurring eye feature change over time. Left column: Each plot displays 4 features best distinguishing responding and nonresponding eyes at baseline (top), month 3 (middle), and month 6 (bottom). Right top: Comparison of baseline and month 3 feature values. Each circle represents the average value of each feature at baseline and month 3. The circles above the line represent a shift toward responding eye feature mean and circles below the line represent a shift toward nonresponding eye feature mean. Right middle: same as the right top, but for month 3 and month 6 recurring features. Right bottom: Same as right top but for baseline and month 6 recurring features.