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Predictive Biomarkers of Age-Related Macular Degeneration Response to Anti-VEGF Treatment.

ABSTRACT: Age-related macular degeneration (AMD) is an incurable disease associated with aging that destroys sharp and central vision. Increasing evidence implicates both systemic and local inflammation in the pathogenesis of AMD. Intravitreal injection of anti-vascular endothelial growth factor (VEGF) agents is currently the first-line therapy for choroidal neovascularization in AMD patients. However, a high number of patients do not show satisfactory responses to anti-VEGF treatment after three injections. Predictive treatment response models are one of the most powerful tools for personalized medicine. Therefore, the application of these models is very helpful to predict the optimal treatment for an early application on each patient. We analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) from AMD patients before treatment to identify biomarkers of response to ranibizumab. A classification model comprised of four mRNAs and one miRNA isolated from PBMCs was able to predict the response to ranibizumab with high accuracy (Area Under the Curve of the Receiver Operating Characteristic curve = 0.968), before treatment. We consider that our classification model, based on mRNA and miRNA from PBMCs allows a robust prediction of patients with insufficient response to anti-VEGF treatment. In addition, it could be used in combination with other methods, such as specific baseline characteristics, to identify patients with poor response to anti-VEGF treatment to establish patient-specific treatment plans at the first visit.

SUBMITTER: Oca AI

PROVIDER: S-EPMC8706948 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.MethodsSD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.ResultsIn a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.ConclusionsThe proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.Translational relevancePrediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Project description:PurposeNo established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.DesignAssessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.ParticipantsEighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).MethodsA total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.Main outcome measuresThe cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.ResultsThe cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.ConclusionsRadiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

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Predictive Biomarkers of Age-Related Macular Degeneration Response to Anti-VEGF Treatment.

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