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.

Role of traditional Chinese medicine in age-related macular degeneration: exploring the gut microbiota’s influence

“…The progression of AMD is significantly influenced by factors such as aging, photodamage, obesity ( Tian et al, 2023 ), dyslipidemia, chronic inflammation ( Ghosh et al, 2022 ; de Almeida Torres et al, 2022 ), hypertension ( Zhang et al, 2022 ), smoking, and internal eye surgery ( Ng Yin Ling et al, 2021 ; Abusharkh et al, 2023 ; Muraleva and Kolosova, 2023 ) ( Figure 1 ). In Western medicine, treatments such as anti-vascular endothelial growth factor (anti-VEGF) ( Al-Zamil and Yassin, 2017 ; Williamson et al, 2023 ), corticosteroids ( Kaya et al, 2019 ), antioxidants ( Evans and Lawrenson, 2017 ; Kushwah et al, 2023 ), laser therapy ( Cohn et al, 2021 ), and photodynamic therapy ( Ando et al, 2023 ) are utilized. Although these approaches can relieve symptoms, they cannot inhibit the development of AMD, and the treatment options for non-neovascular AMD are particularly limited.…”