• Quantitative relative reflectivity features derived from OCT images provide novel, non-invasive biomarkers for predicting short-term anti-VEGF response in diabetic macular edema. • Reflectivity variation metrics of the largest intraretinal cystoid spaces show strong discriminative power between persistent and non-persistent DME. • Machine learning models incorporating OCT reflectivity features achieve high predictive performance, with simpler models offering improved robustness and interpretability. To develop and validate machine learning (ML) models using optical coherence tomography (OCT)–derived quantitative relative reflectivity (RR) features to predict short-term response to anti–vascular endothelial growth factor (anti-VEGF) therapy in diabetic macular edema (DME), and to identify non-invasive imaging biomarkers for treatment stratification. This retrospective study included 345 eyes from 345 patients with DME who received three consecutive monthly intravitreal anti-VEGF injections. Based on 3-month anatomical and functional outcomes, eyes were classified as Non-Persistent DME (NPDME, n=184) or Persistent DME (PDME, n=161). A total of 30 baseline features were extracted, comprising clinical data, OCT morphological characteristics, and fundamental reflectivity measurements. From these, we derived additional quantitative RR features via predefined mathematical transformations. After feature engineering and ensemble feature selection, 25 predictors were retained for model development. Six ML models including logistic regression (LR), random forest (RF), gradient boosting (GB), multilayer perceptron (MLP), stacking ensemble, and voting ensemble, were evaluated on an independent test set (n=69) using area under the curve (AUC), sensitivity, and specificity. Key RR features, particularly those describing the largest intraretinal cystoid spaces (LICS), showed significant differences between groups (p<0.001). The stacking ensemble model achieved the highest discriminative ability, with an AUC of 0.934 (95% CI: 0.867–0.987), a sensitivity of 81.08%, and a specificity of 90.62%. After threshold optimization, the GB model demonstrated the highest sensitivity (97.30%) with an AUC of 0.931 (95% CI: 0.865–0.984), while the LR model exhibited the most favorable generalization (lowest overfitting risk). Quantitative OCT-derived RR features, especially those reflecting intraretinal cyst characteristics, are strongly associated with short-term anti-VEGF response in DME. ML models incorporating these features may support individualized treatment assessment, with simpler models offering advantages in robustness and interpretability.
Zhou et al. (Sun,) studied this question.