Computational Imaging Biomarker Correlation with Intraocular Cytokine Expression in Diabetic Macular Edema

Radiomics in ophthalmology: a systematic review

BACKGROUND

Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology.

METHOD

Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS).

RESULTS

A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation.

CONCLUSIONS

Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement.

CLINICAL RELEVANCE STATEMENT

Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis.

KEY POINTS

Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.

Relationship between ischemic index, leakage index, and macular edema in branch retinal vein occlusion

PURPOSE

To investigate the combined association of the ischemic index and leakage index with macular edema on ultra-widefield fluorescein angiography (UWFFA) in patients with branch retinal vein occlusion (BRVO).

METHODS

Retrospective image analysis study. The leakage index and ischemic index were calculated using Fiji after aligning early and late UWFFA images. Differences in the ischemic index, leakage index, and central macular thickness (CMT) between ischemic and non-ischemic BRVO were compared. Moreover, the association between the ischemic index, leakage index, and macular edema was analyzed.

RESULTS

Eighty-three patients with BRVO were enrolled, including 53 non-ischemic BRVO and 30 ischemic BRVO patients. No significant differences were observed in leakage index and CMT between ischemic BRVO and non-ischemic BRVO (all P > 0.05). In all included patients, CMT correlated with the panretina and all subregion leakage indexes (all P < 0.01), but not with the ischemic index (all P > 0.05). In the ischemic BRVO group, CMT showed a correlation with the leakage index in several regions, but not with the ischemic index. After adjusting for the ischemic index and other clinical features, CMT remained significantly correlated with the leakage index in all regions.

CONCLUSION

The leakage index may be a more effective biomarker for monitoring BRVO-associated macular edema compared to the ischemic index. Further follow-up studies are warranted to validate these findings.

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

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.