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Updated: Mar 29, 2026
Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
Published on: February 18, 2022
Quantitative relative reflectivity features based on OCT for predicting the efficacy of anti-VEGF therapy in diabetic
Guanghua Zhou1, Xiaoshan Lin2, Xiaolin Yan3
1Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China; Department of Ophthalmology, The Fifth Affiliated Hospital, Southern Medical University, Guangzhou 510900, China.
Objective:
To develop and validate machine learning (ML) models using optical coherence tomography (OCT)-derived quantitative relative reflectivity (QRR) 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.
Methods:
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. We derived additional QRR 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.
Results:
Key QRR 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).
Conclusions:
OCT-derived QRR features, especially those reflecting intraretinal cyst characteristics, are strongly associated with short-term anti-VEGF response in DME. ML models incorporating these features can support individualized treatment assessment, with simpler models offering advantages in robustness and interpretability.
