Abstract
Anti-vascular endothelial growth factor (VEGF) agents are widely regarded as the first line of therapy for diabetic macular edema (DME) but are not universally effective. An automatic method that can predict whether a patient is likely to respond to anti-VEGF therapy can avoid unnecessary trial and error treatment strategies and promote the selection of more effective first-line therapies. The objective of this study is to automatically predict the efficacy of anti-VEGF treatment of DME in individual patients based on optical coherence tomography (OCT) images. We performed a retrospective study of 127 subjects treated for DME with three consecutive injections of anti-VEGF agents. Patients' retinas were imaged using spectral-domain OCT (SD-OCT) before and after anti-VEGF therapy, and the total retinal thicknesses before and after treatment were extracted from OCT B-scans. A novel deep convolutional neural network was designed and evaluated using pre-treatment OCT scans as input and differential retinal thickness as output, with 5-fold cross-validation. The group of patients responsive to anti-VEGF treatment was defined as those with at least a 10% reduction in retinal thickness following treatment. The predictive performance of the system was evaluated by calculating the precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The algorithm achieved an average AUC of 0.866 in discriminating responsive from non-responsive patients, with an average precision, sensitivity, and specificity of 85.5%, 80.1%, and 85.0%, respectively. Classification precision was significantly higher when differentiating between very responsive and very unresponsive patients. The proposed automatic algorithm accurately predicts the response to anti-VEGF treatment in DME patients based on OCT images. This pilot study is a critical step toward using non-invasive imaging and automated analysis to select the most effective therapy for a patient's specific disease condition.
Highlights
Diabetic macular edema (DME) is a major cause of central vision loss in patients with diabetic retinopathy if untreated [1]
We address whether a DME patient’s response to anti-vascular endothelial growth factor (VEGF) therapy can be predicted prior to treatment based on pretreatment optical coherence tomography (OCT) images
For the univariate feature selection (UFS) method, Chi-square ( χ2) and mutual information (MI) statistics were used to examine each feature individually to determine the dependency between features and labels
Summary
Diabetic macular edema (DME) is a major cause of central vision loss in patients with diabetic retinopathy if untreated [1]. Intravitreal anti-VEGF agents are the most common first line of therapy for DME, but not every patient responds to them [3] and other forms of treatment are often required [4,5]. Previous randomized clinical trials have demonstrated that a subset of patients responds well to any given treatment modality [6]. The challenge of selecting the optimal treatment modality a priori remains a clinically unmet need, and many clinicians utilize a trial and error approach in which anti-VEGF therapy is first-line for all patients with alternatives utilized following treatment failure. It is of great interest to predict a priori whether anti-VEGF treatment will be effective for a particular patient. The single-shot term in our study emphasizes that the proposed method predicts the response to therapy by analyzing a single timepoint pre-treatment OCT volume, without the need for longitudinal treatment information such as time-series OCT images, patient records, or other metadata
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
