Radiomics-Based Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration With Pigment Epithelial Detachment

Radiomics-Based OCT Analysis of Choroid Reveals Biomarkers of Central Serous Chorioretinopathy

Purpose

Biomarkers from choroidal imaging can enhance clinical decision-making for chorioretinal disease; however, identification of biomarkers is labor-intensive and limited by human intuition. Here we apply radiomics feature extraction to choroid imaging from swept-source optical coherence tomography (SS-OCT) to automatically identify biomarkers that distinguish healthy, central serous chorioretinopathy (CSCR), and unaffected fellow eyes.

Methods

Radiomics features were extracted from SS-OCT images from healthy (n = 30), CSCR (n = 39), and unaffected fellow eyes (n = 20), with a total of 44,500 single-cross sectional horizontal images and 8900 en face images. Logistic regression classification of eyes as healthy versus CSCR, healthy versus fellow, or CSCR versus fellow was performed using radiomics features. Statistical significance was determined using 95% bootstrap confidence intervals.

Results

Significant differences between healthy and CSCR eyes were found for all radiomics feature groups. Classification of health versus CSCR achieved classification accuracy of 84.2% (77.2%-89.9%) in horizontal images and 85.3% (78.2%-90.7%) in en face images. For en face images, classification accuracy increased by 1.02% (0.50%-1.53%) for every 10% increase in choroid depth. Fellow eye classification using a classifier trained to distinguish healthy and CSCR eyes resulted in 90.4% (90.2%-90.6%) of horizontal images and 90.2% (89.8%-90.2%) of en face images being classified as CSCR.

Conclusions

These results demonstrate accurate classification of healthy and CSCR eyes using choroid OCT radiomics features. Furthermore, radiomics features revealed signatures of CSCR in unaffected fellow eyes.

Translational Relevance

These findings demonstrate the potential for radiomics features in clinical decision support for CSCR.

Bioinformatics Analysis of Lactylation-related Biomarkers and Potential Pathogenesis Mechanisms in Age-related Macular Degeneration

Background

Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.

Objectives

The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.

Methods

Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.

Results

A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including HMGN2, TOP2B, HNRNPH1, SF3A1, SRRM2, HIST1H1C, and HIST1H2BD were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.

Conclusion

We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.

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

The pathogenesis of age-related macular degeneration (AMD), a degenerative retinopathy, remains unclear. Administration of anti-vascular endothelial growth factor agents, antioxidants, fundus lasers, photodynamic therapy, and transpupillary warming has proven effective in alleviating symptoms; however, these interventions cannot prevent or reverse AMD. Increasing evidence suggests that AMD risk is linked to changes in the composition, abundance, and diversity of the gut microbiota (GM). Activation of multiple signaling pathways by GM metabolites, including lipopolysaccharides, oxysterols, short-chain fatty acids (SCFAs), and bile acids (BAs), influences retinal physiology. Traditional Chinese medicine (TCM), known for its multi-component and multi-target advantages, can help treat AMD by altering GM composition and regulating the levels of certain substances, such as lipopolysaccharides, reducing oxysterols, and increasing SCFA and BA contents. This review explores the correlation between GM and AMD and interventions for the two to provide new perspectives on treating AMD with TCM.