Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial

Abstract

Purpose: The goal of this study was to evaluate the role of texture-based baseline radiomic features (Fr) and dynamic radiomics alterations (delta, FΔr) within multiple targeted compartments on optical coherence tomography (OCT) scans to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (nAMD).

Methods: HAWK is a phase 3 clinical trial data set of active nAMD patients (N = 1082) comparing brolucizumab and aflibercept. This analysis included patients receiving 6 mg brolucizumab or 2 mg aflibercept and categorized as complete responders (n = 280) and incomplete responders (n = 239) based on whether or not the eyes achieved/maintained fluid resolution on OCT. A total of 481 Fr were extracted from each of the fluid, subretinal hyperreflective material (SHRM), retinal tissue, and sub-retinal pigment epithelium (RPE) compartments. Most discriminating eight baseline features, selected by the minimum redundancy, maximum relevance feature selection, were evaluated using a quadratic discriminant analysis (QDA) classifier on the training set (Str, n = 363) to differentiate between the two patient groups. Classifier performance was subsequently validated on independent test set (St, n = 156).

Results: In total, 519 participants were included in this analysis from the HAWK phase 3 study. There were 280 complete responders and 219 incomplete responders. Compartmental analysis of radiomics featured identified the sub-RPE and SHRM compartments as the most distinguishing between the two response groups. The QDA classifier yielded areas under the curve of 0.78, 0.79, and 0.84, respectively, using Fr, FΔr, and combined Fr, FΔr, and Fc on St.

Conclusions: Utilizing compartmental static and dynamic radiomics features, unique differences were identified between eyes that respond differently to anti-VEGF therapy in a large phase 3 trial that may provide important predictive value.

Translational relevance: Imaging biomarkers, such as radiomics features identified in this analysis, for predicting treatment response are needed to enhanced precision medicine in the management of nAMD.

Figure 1.

Flowchart showing inclusion and exclusion criteria for the study. BCVA, best-corrected visual acuity; GA, geographic atrophy; SD-OCT, spectral domain optical coherence tomography.

Figure 2.

Overall pipeline of the radiomics-based assessment anti-VEGF therapy treatment response in nAMD using baseline OCT scans. (a) OCT scans of the HAWK study were retrospectively collected. (b) Individual fluid and SHRM compartments and the retinal tissue compartments (ILM-RPE, RPE-BM) were partitioned. (c) Texture-based radiomic features were extracted from the individual OCT and retinal tissue compartments using MATLAB V.2022b. For each of the individual compartments, feature statistics of median, standard deviation, skewness, and kurtosis were computed. (d) Top eight features were selected by feature selection and evaluated in conjunction with an ML classifier in a threefold cross-validation setting on the training set. (e) Classifier performance was evaluated on the test set. EZ, ellipsoid zone.

Figure 3.

Illustration of the discriminability of the “Laws E3S3L3” texture feature within the sub-RPE compartment on baseline OCT scans: segmentation of sub-RPE (RPE-BM) compartment for one case of (a) complete responder and (b) one case of an incomplete responder. (c, d) Zoomed-in sub-RPE compartment for (a) and (b), respectively. (e, f) Visualization of the heatmap of the most discriminating feature (Laws E3S3L3) expression on baseline OCT scan for (c) complete responder and (d) incomplete responder, respectively. Prevalence of warmer color tones in the feature expression of the complete responder is reflective of a higher order of heterogeneity within the sub-RPE compartment texture for the complete responder.

Figure 4.

The box-and-whisker plot of the most discriminating feature (a) baseline sub-RPE skewness Laws E3S3L3 (identified from experiment 1) and (b) SHRM delta skewness Laws L3E3S3 (identified from experiment 2). The plot on the left corresponds to the feature values from the complete responders (n = 280) and that on the right corresponds to the feature values from the incomplete responders (n = 239).

Figure 5.

Illustration of the discriminability of the “Laws L3E3S3” texture feature within the SHRM compartment on baseline and posttherapy OCT scans: segmentation of SHRM compartment for one case of (a) a complete responder and one case of (b) an incomplete responder for baseline and posttherapy (month 3) OCT scans. (c, d) Zoomed-in SHRM compartment for (a) and (b), respectively. Visualization of the heatmap of the most discriminating delta texture feature (Laws L3E3S3) expression on baseline and posttherapy OCT scans for one case of (e) a complete responder and one case of (f) an incomplete responder. The heterogeneity within the texture is reflected by warmer color tones, whereas the cooler color tones represent the texture is more homogeneous. The Laws energy feature captures the textural alteration within the SHRM compartment following therapy for the complete responder.