Biomarkers to Inform Prognosis and Treatment for Unresectable or Metastatic GEP-NENs.

Importance: Evidence-based treatment decisions for advanced gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) require individualized patient-centered decision-making that accounts for patient and cancer characteristics. Objective: To create an accessible guidance document to educate clinicians and patients on biomarkers informing prognosis and treatment in unresectable or metastatic GEP-NENs. Methods: A multidisciplinary panel in-person workshop was convened to define methods. English language articles published from January 2016 to January 2023 in PubMed (MEDLINE) and relevant conference abstracts were reviewed to investigate prognostic and treatment-informing features in unresectable or metastatic GEP-NENs. Data from included studies were used to form evidence-based recommendations. Quality of evidence and strength of recommendations were determined using the Grading of Recommendations, Assessment, Development and Evaluations framework. Consensus was reached via electronic survey following a modified Delphi method. Findings: A total of 131 publications were identified, including 8 systematic reviews and meta-analyses, 6 randomized clinical trials, 29 prospective studies, and 88 retrospective cohort studies. After 2 rounds of surveys, 24 recommendations and 5 good clinical practice statements were developed, with full consensus among panelists. Recommendations focused on tumor and functional imaging characteristics, blood-based biomarkers, and carcinoid heart disease. A single strong recommendation was made for symptomatic carcinoid syndrome informing treatment in midgut neuroendocrine tumors. Conditional recommendations were made to use grade, morphology, primary site, and urinary 5-hydroxyindoleacetic levels to inform treatment. The guidance document was endorsed by the Commonwealth Neuroendocrine Tumour Collaboration and the North American Neuroendocrine Tumor Society. Conclusions and Relevance: The study results suggest that select factors have sufficient evidence to inform care in GEP-NENs, but the evidence for most biomarkers is weak. This article may help guide management and identify gaps for future research to advance personalized medicine and improve outcomes for patients with GEP-NENs.

Artificial intelligence for retinal diseases.

PURPOSE: To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases. METHODS: We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search terms included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review articles. RESULTS: Research studies have investigated and shown the utility of AI for screening for diseases such as DR, AMD, ROP, and SCR. Research studies using validated and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research articles suggest AI may be useful for planning and performing robotic surgery. Studies suggest AI holds the potential to help lessen the impact of socioeconomic disparities on the outcomes of retinal diseases. CONCLUSIONS: AI applications for retinal diseases can assist the clinician, not only by disease screening and monitoring for disease recurrence but also in quantitative analysis of treatment outcomes and prediction of treatment response. The public health impact on the prevention of blindness from DR, AMD, and other retinal vascular diseases remains to be determined.

Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.

Purpose: This study aimed to investigate the impact of distinctive capillary-large vessel (CLV) analysis in optical coherence tomography angiography (OCTA) on the classification performance of diabetic retinopathy (DR). Methods: This multicenter study analyzed 212 OCTA images from 146 patients, including 28 controls, 36 diabetic patients without DR (NoDR), 31 with mild non-proliferative DR (NPDR), 28 with moderate NPDR, and 23 with severe NPDR. Quantitative features were derived from the whole image as well as the parafovea and perifovea regions. A support vector machine classifier was employed for DR classification. The accuracy and area under the receiver operating characteristic curve were used to evaluate the classification performance, utilizing features derived from the whole image and specific regions, both before and after CLV analysis. Results: Differential CLV analysis significantly improved OCTA classification of DR. In binary classifications, accuracy improved by 11.81%, rising from 77.45% to 89.26%, when utilizing whole image features. For multiclass classifications, accuracy increased by 7.55%, from 78.68% to 86.23%. Incorporating features from the whole image, parafovea, and perifovea further improved binary classification accuracy from 83.07% to 93.80%, and multiclass accuracy from 82.64% to 87.92%. Conclusions: This study demonstrated that feature changes in capillaries are more sensitive during DR progression, and CLV analysis can significantly improve DR classification performance by extracting features that are specific to large vessels and capillaries in OCTA. Incorporating regional features further improves DR classification accuracy. Differential CLV analysis promises better disease screening, diagnosis, and treatment outcome assessment.

Response of Diabetic Macular Edema to Anti-VEGF Medications Correlates with Improvement in Macular Vessel Architecture Measured with OCT Angiography.

Purpose: Improvements in best-corrected visual acuity (BCVA) and central subfield thickness (CST) have been well documented after intravitreal injection of anti-VEGF medications in diabetic macular edema (DME); however, their effect on the vasculature of the macula in diabetic retinopathy (DR) remains poorly understood. Our aim was to explore the effect of intravitreal injection of anti-VEGF on parameters of retinal vascular microstructure in DR with OCT angiography (OCTA). Design: Retrospective study of adult patients with DME that were treated with anti-VEGF intravitreal injections at the University of Illinois at Chicago between 2017 and 2022. Participants: Forty-one eyes from 30 patients with nonproliferative or proliferative DR with a mean age of 58.83 ± 11.71 years, mean number of intravitreal injections of 2.8 ± 1.4, and mean follow-up of 6.5 ± 1.7 months. Methods: ImageJ was employed to measure parameters of retinal vascular microstructure in OCTA images, which included perfusion density, vessel-length density (VLD), vessel diameter, and foveal avascular zone (FAZ) characteristics (area, perimeter, and circularity). Student t tests and analysis of variance were used to determine statistical significance. Main Outcome Measures: A primary analysis was performed comparing the mean of each parameter of all patients as a single group at the beginning and end of the study period. A subgroup analysis was then performed after stratifying patients based on visual improvement, change in CST, prior injection history, and number of injections. Results: Eyes demonstrated statistical improvement in BCVA logarithm of the minimum angle of resolution score and CST after anti-VEGF treatment. Primary analysis showed a reduction in the vessel diameter of the superficial and deep retinal vasculature, as well as an increase in the circularity of the FAZ within the superficial retinal vasculature after anti-VEGF treatment. Subgroup analysis revealed that eyes with improvement in BCVA exhibited reduced vessel diameter in the superficial retinal vasculature and that eyes with the largest decrease in CST displayed increased perfusion density and VLD in the deep retinal vasculature. Conclusions: Intravitreal injection of anti-VEGF agents to treat DME improved parameters of retinal vascular microstructure on OCTA over a period of 3 to 9 months, and this effect was most pronounced in eyes that experienced improvement in BCVA and CST. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.

This study investigates the impact of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) on machine learning classification of diabetic retinopathy (DR). Leveraging deep learning for arterial-venous area (AVA) segmentation, six quantitative features, including perfusion intensity density (PID), blood vessel density (BVD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI) features, were derived from OCTA images before and after AV differentiation. A support vector machine (SVM) classifier was utilized to assess both binary and multiclass classifications of control, diabetic patients without DR (NoDR), mild DR, moderate DR, and severe DR groups. Initially, one-region features, i.e., quantitative features extracted from the entire OCTA, were evaluated for DR classification. Differential AV analysis improved classification accuracies from 78.86% to 87.63% and from 79.62% to 85.66% for binary and multiclass classifications, respectively. Additionally, three-region features derived from the entire image, parafovea, and perifovea, were incorporated for DR classification. Differential AV analysis further enhanced classification accuracies from 84.43% to 93.33% and from 83.40% to 89.25% for binary and multiclass classifications, respectively. These findings highlight the potential of differential AV analysis in augmenting disease diagnosis and treatment assessment using OCTA.

Faricimab for neovascular age-related macular degeneration and diabetic macular edema: from preclinical studies to phase 3 outcomes.

Intravitreal anti-vascular endothelial growth factor (VEGF) therapy is the standard of care for diabetic macular edema (DME) and neovascular age-related macular degeneration (nAMD); however, vision gains and anatomical improvements are not sustained over longer periods of treatment, suggesting other relevant targets may be needed to optimize treatments. Additionally, frequent intravitreal injections can prove a burden for patients and caregivers. Angiopoietin-2 (Ang-2) has been explored as an additional therapeutic target, due to the involvement of Ang-2 in DME and nAMD pathogenesis. Recent evidence supports the hypothesis that targeting both VEGF and Ang-2 may improve clinical outcomes in DME and nAMD compared with targeting VEGF alone by enhancing vascular stability, resulting in reduced macular leakage, prevention of neovascularization, and diminished inflammation. Faricimab, a novel bispecific antibody that targets VEGF-A and Ang-2, has been evaluated in clinical trials for DME (YOSEMITE/RHINE) and nAMD (TENAYA/LUCERNE). These trials evaluated faricimab against the anti-VEGFA/B and anti-placental growth factor fusion protein aflibercept, both administered by intravitreal injection. In addition to faricimab efficacy, safety, and pharmacokinetics, durability was evaluated during the trials using a treat-and-extend regimen. At 1 year, faricimab demonstrated non-inferior vision gains versus aflibercept across YOSEMITE/RHINE and TENAYA/LUCERNE. In YOSEMITE/RHINE, faricimab improved anatomic parameters versus aflibercept. Reduction of central subfield thickness (CST), and absence of both DME and intraretinal fluid were greater in faricimab- versus aflibercept-treated eyes. In TENAYA/LUCERNE, CST reductions were greater for faricimab than aflibercept at the end of the head-to-head phase (0-12 weeks), and were comparable with aflibercept at year 1, but with less frequent dosing. CST and vision gains were maintained during year 2 of both YOSEMITE/RHINE and TENAYA/LUCERNE. These findings suggest that dual Ang-2/VEGF-A pathway inhibition may result in greater disease control versus anti-VEGF alone, potentially addressing the unmet needs and reducing treatment burden, and improving real-world outcomes and compliance in retinal vascular diseases. Long-term extension studies (RHONE-X, AVONELLE-X) are ongoing. Current evidence suggests that dual inhibition with faricimab heralds the beginning of multitargeted treatment strategies inhibiting multiple, independent components of retinal pathology, with faricimab providing opportunities to reduce treatment burden and improve outcomes compared with anti-VEGF monotherapy.

Quantitative Characterization of Retinal Features in Translated OCTA.

Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware. Methods: The method involved implementing a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images. The validation employs several quality and quantitative metrics to compare the translated images with ground truth OCTAs (GT-OCTA). We then quantitatively characterize vascular features generated in TR-OCTAs with GT-OCTAs to assess the feasibility of using TR-OCTA for objective disease diagnosis. Result: TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution, moderate structural similarity and contrast quality compared to GT-OCTAs). There were slight discrepancies in vascular metrics, especially in diseased patients. Blood vessel features like tortuosity and vessel perimeter index showed a better trend compared to density features which are affected by local vascular distortions. Conclusion: This study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection. Translation relevance: This study has the potential to significantly enhance the diagnostic process for retinal diseases by making detailed vascular imaging more widely available and reducing dependency on costly OCTA equipment.

TENAYA and LUCERNE: Two-Year Results from the Phase 3 Neovascular Age-Related Macular Degeneration Trials of Faricimab with Treat-and-Extend Dosing in Year 2.

PURPOSE: To evaluate 2-year efficacy, durability, and safety of the bispecific antibody faricimab, which inhibits both angiopoietin-2 and VEGF-A. DESIGN: TENAYA (ClinicalTrials.gov identifier, NCT03823287) and LUCERNE (ClinicalTrials.gov identifier, NCT03823300) were identically designed, randomized, double-masked, active comparator-controlled phase 3 noninferiority trials. PARTICIPANTS: Treatment-naive patients with neovascular age-related macular degeneration (nAMD) 50 years of age or older. METHODS: Patients were randomized (1:1) to intravitreal faricimab 6.0 mg up to every 16 weeks (Q16W) or aflibercept 2.0 mg every 8 weeks (Q8W). Faricimab fixed dosing based on protocol-defined disease activity at weeks 20 and 24 up to week 60, followed up to week 108 by a treat-and-extend personalized treatment interval regimen. MAIN OUTCOME MEASURES: Efficacy analyses included change in best-corrected visual acuity (BCVA) from baseline at 2 years (averaged over weeks 104, 108, and 112) and proportion of patients receiving Q16W, every 12 weeks (Q12W), and Q8W dosing at week 112 in the intention-to-treat population. Safety analyses included ocular adverse events (AEs) in the study eye through study end at week 112. RESULTS: Of 1326 patients treated across TENAYA/LUCERNE, 1113 (83.9%) completed treatment (n = 555 faricimab; n = 558 aflibercept). The BCVA change from baseline at 2 years was comparable between faricimab and aflibercept groups in TENAYA (adjusted mean change, +3.7 letters [95% confidence interval (CI), +2.1 to +5.4] and +3.3 letters [95% CI, +1.7 to +4.9], respectively; mean difference, +0.4 letters [95% CI, -1.9 to +2.8]) and LUCERNE (adjusted mean change, +5.0 letters [95% CI, +3.4 to +6.6] and +5.2 letters [95% CI, +3.6 to +6.8], respectively; mean difference, -0.2 letters [95% CI, -2.4 to +2.1]). At week 112 in TENAYA and LUCERNE, 59.0% and 66.9%, respectively, achieved Q16W faricimab dosing, increasing from year 1, and 74.1% and 81.2%, achieved Q12W or longer dosing. Ocular AEs in the study eye were comparable between faricimab and aflibercept groups in TENAYA (55.0% and 56.5% of patients, respectively) and LUCERNE (52.9% and 47.5% of patients, respectively) through week 112. CONCLUSIONS: Treat-and-extend faricimab treatment based on nAMD disease activity maintained vision gains through year 2, with most patients achieving extended dosing intervals. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

METformin for the MINimization of Geographic Atrophy Progression (METforMIN): A Randomized Trial.

Purpose: Metformin use has been associated with a decreased risk of age-related macular degeneration (AMD) progression in observational studies. We aimed to evaluate the efficacy of oral metformin for slowing geographic atrophy (GA) progression. Design: Parallel-group, multicenter, randomized phase II clinical trial. Participants: Participants aged ≥ 55 years without diabetes who had GA from atrophic AMD in ≥ 1 eye. Methods: We enrolled participants across 12 clinical centers and randomized participants in a 1:1 ratio to receive oral metformin (2000 mg daily) or observation for 18 months. Fundus autofluorescence imaging was obtained at baseline and every 6 months. Main Outcome Measures: The primary efficacy endpoint was the annualized enlargement rate of the square root-transformed GA area. Secondary endpoints included best-corrected visual acuity (BCVA) and low luminance visual acuity (LLVA) at each visit. Results: Of 66 enrolled participants, 34 (57 eyes) were randomized to the observation group and 32 (53 eyes) were randomized to the treatment group. The median follow-up duration was 13.9 and 12.6 months in the observation and metformin groups, respectively. The mean ± standard error annualized enlargement rate of square root transformed GA area was 0.35 ± 0.04 mm/year in the observation group and 0.42 ± 0.04 mm/year in the treatment group (risk difference = 0.07 mm/year, 95% confidence interval = -0.05 to 0.18 mm/year; P = 0.26). The mean ± standard error decline in BCVA was 4.8 ± 1.7 letters/year in the observation group and 3.4 ± 1.1 letters/year in the treatment group (P = 0.56). The mean ± standard error decline in LLVA was 7.3 ± 2.5 letters/year in the observation group and 0.8 ± 2.2 letters/year in the treatment group (P = 0.06). Fourteen participants in the metformin group experienced nonserious adverse events related to metformin, with gastrointestinal side effects as the most common. No serious adverse events were attributed to metformin. Conclusions: The results of this trial as conducted do not support oral metformin having effects on reducing the progression of GA. Additional placebo-controlled trials are needed to explore the role of metformin for AMD, especially for earlier stages of the disease. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.

The purpose of this study is to evaluate layer fusion options for deep learning classification of optical coherence tomography (OCT) angiography (OCTA) images. A convolutional neural network (CNN) end-to-end classifier was utilized to classify OCTA images from healthy control subjects and diabetic patients with no retinopathy (NoDR) and non-proliferative diabetic retinopathy (NPDR). For each eye, three en-face OCTA images were acquired from the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CC) layers. The performances of the CNN classifier with individual layer inputs and multi-layer fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. For individual layer inputs, the superficial OCTA was observed to have the best performance, with 87.25% accuracy, 78.26% sensitivity, and 90.10% specificity, to differentiate control, NoDR, and NPDR. For multi-layer fusion options, the best option is the intermediate-fusion architecture, which achieved 92.65% accuracy, 87.01% sensitivity, and 94.37% specificity. To interpret the deep learning performance, the Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to identify spatial characteristics for OCTA classification. Comparative analysis indicates that the layer data fusion options can affect the performance of deep learning classification, and the intermediate-fusion approach is optimal for OCTA classification of DR.

Federated learning for diagnosis of age-related macular degeneration.

This paper presents a federated learning (FL) approach to train deep learning models for classifying age-related macular degeneration (AMD) using optical coherence tomography image data. We employ the use of residual network and vision transformer encoders for the normal vs. AMD binary classification, integrating four unique domain adaptation techniques to address domain shift issues caused by heterogeneous data distribution in different institutions. Experimental results indicate that FL strategies can achieve competitive performance similar to centralized models even though each local model has access to a portion of the training data. Notably, the Adaptive Personalization FL strategy stood out in our FL evaluations, consistently delivering high performance across all tests due to its additional local model. Furthermore, the study provides valuable insights into the efficacy of simpler architectures in image classification tasks, particularly in scenarios where data privacy and decentralization are critical using both encoders. It suggests future exploration into deeper models and other FL strategies for a more nuanced understanding of these models' performance. Data and code are available at https://github.com/QIAIUNCC/FL_UNCC_QIAI.

Simulation of long-term impact of intravitreal anti-VEGF therapy on patients with severe non-proliferative diabetic retinopathy.

OBJECTIVE: A simulation model was constructed to assess long-term outcomes of proactively treating severe non-proliferative diabetic retinopathy (NPDR) with anti-vascular endothelial growth factor (anti-VEGF) therapy versus delaying treatment until PDR develops. METHODS AND ANALYSIS: Simulated patients were generated using a retrospective real-world cohort of treatment-naive patients identified in an electronic medical records database (IBM Explorys) between 2011 and 2017. Impact of anti-VEGF treatment was derived from clinical trial data for intravitreal aflibercept (PANORAMA) and ranibizumab (RISE/RIDE), averaged by weighted US market share. Real-world risk of PDR progression was modelled using Cox multivariable regression. The Monte Carlo simulation model examined rates of progression to PDR and sustained blindness (visual acuity <20/200) for 2 million patients scaled to US NPDR disease prevalence. Simulated progression rates from severe NPDR to PDR over 5 years and blindness rates over 10 years were compared for delayed versus early-treatment patients. RESULTS: Real-world data from 77 454 patients with mild-to-severe NPDR simulated 2 million NPDR patients, of which 86 680 had severe NPDR. Early treatment of severe NPDR with anti-VEGF therapy led to a 51.7% relative risk reduction in PDR events over 5 years (15 704 early vs 32 488 delayed), with a 19.4% absolute risk reduction (18.1% vs 37.5%). Sustained blindness rates at 10 years were 4.4% for delayed and 1.9% for early treatment of severe NPDR. CONCLUSION: The model suggests treating severe NPDR early with anti-VEGF therapy, rather than delaying treatment until PDR develops, could significantly reduce PDR incidence over 5 years and sustained blindness over 10 years.

Transitioning single-cell genomics into the clinic.

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.

Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models.

Diabetic retinopathy (DR) is a major cause of vision impairment in diabetic patients worldwide. Due to its prevalence, early clinical diagnosis is essential to improve treatment management of DR patients. Despite recent demonstration of successful machine learning (ML) models for automated DR detection, there is a significant clinical need for robust models that can be trained with smaller cohorts of dataset and still perform with high diagnostic accuracy in independent clinical datasets (i.e., high model generalizability). Towards this need, we have developed a self-supervised contrastive learning (CL) based pipeline for classification of referable vs non-referable DR. Self-supervised CL based pretraining allows enhanced data representation, therefore, the development of robust and generalized deep learning (DL) models, even with small, labeled datasets. We have integrated a neural style transfer (NST) augmentation in the CL pipeline to produce models with better representations and initializations for the detection of DR in color fundus images. We compare our CL pretrained model performance with two state of the art baseline models pretrained with Imagenet weights. We further investigate the model performance with reduced labeled training data (down to 10 percent) to test the robustness of the model when trained with small, labeled datasets. The model is trained and validated on the EyePACS dataset and tested independently on clinical datasets from the University of Illinois, Chicago (UIC). Compared to baseline models, our CL pretrained FundusNet model had higher area under the receiver operating characteristics (ROC) curve (AUC) (CI) values (0.91 (0.898 to 0.930) vs 0.80 (0.783 to 0.820) and 0.83 (0.801 to 0.853) on UIC data). At 10 percent labeled training data, the FundusNet AUC was 0.81 (0.78 to 0.84) vs 0.58 (0.56 to 0.64) and 0.63 (0.60 to 0.66) in baseline models, when tested on the UIC dataset. CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.

Normalized Blood Flow Index in Optical Coherence Tomography Angiography Provides a Sensitive Biomarker of Early Diabetic Retinopathy.

Purpose: To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR). Methods: Optical coherence tomography angiography (OCTA) images of healthy controls, diabetic patients without DR (NoDR), and patients with mild nonproliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm × 6 mm area. Enface projections of the superficial vascular plexus (SVP) and the deep capillary plexus (DCP) were obtained for the quantitative OCTA feature analysis. Three quantitative OCTA features were examined: blood vessel density (BVD), blood flow flux (BFF), and NBFI. Each feature was calculated from both the SVP and DCP and their sensitivities to distinguish the three cohorts of the study were evaluated. Results: The only quantitative feature capable of distinguishing all three cohorts was NBFI in the DCP image. Comparative study revealed that both BVD and BFF were able to distinguish the controls and NoDR from mild NPDR. However, neither BVD nor BFF was sensitive enough to separate NoDR from the healthy controls. Conclusions: The NBFI has been demonstrated as a sensitive biomarker of early DR, revealing retinal blood flow abnormality better than traditional BVD and BFF. The NBFI in the DCP was verified as the most sensitive biomarker, supporting that diabetes affects the DCP earlier than SVP in DR. Translational Relevance: NBFI provides a robust biomarker for quantitative analysis of DR-caused blood flow abnormalities, promising early detection and objective classification of DR.

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography.

BACKGROUND: Differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity. METHOD: A deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR. RESULTS: It was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups. CONCLUSIONS: AVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.

Some people with diabetes develop diabetic retinopathy, in which the blood flow through the eye changes, resulting in damage to the back of the eye, called the retina. Changes in blood flow can be measured by imaging the eye using a method called optical coherence tomography angiography (OCTA). The authors developed a computer program named AVA-Net that determines changes in blood flow through the eye from OCTA images. The program was tested on images from people with healthy eyes, people with diabetes but no eye disease, and people with mild diabetic retinopathy. Their program found differences between these groups and so could be used to improve diagnosis of people with diabetic retinopathy.

Visual Field Abnormalities in Early-Stage Diabetic Retinopathy Assessed by Chromatic Perimetry.

Purpose: The purpose of this study was to define the nature and extent of sensitivity loss using chromatic perimetry in diabetics who have mild or no retinopathy. Methods: Thirty-four individuals with type II diabetes mellitus who have mild nonproliferative diabetic retinopathy (MDR; N = 17) or no diabetic retinopathy (NDR; N = 17) and 15 visually normal, non-diabetic controls participated. Sensitivity was assessed along the horizontal visual field meridian using an Octopus 900 perimeter. Measurements were performed under light- and dark-adapted conditions using long-wavelength (red) and short-wavelength (blue) Goldmann III targets. Cumulative defect curves (CDCs) were constructed to determine whether field sensitivity loss was diffuse or localized. Results: Sensitivity was reduced significantly under light-adapted conditions for both stimulus colors for the NDR (mean defect ± SEM = -2.1 dB ± 0.6) and MDR (mean defect ± SEM = -4.0 dB ± 0.7) groups. Sensitivity was also reduced under dark-adapted conditions for both stimulus colors for the NDR (mean defect ± SEM = -1.9 dB ± 0.7) and MDR (mean defect ± SEM = -4.5 ± 1.0 dB) groups. For both diabetic groups, field loss tended to be diffuse under light-adapted conditions (up to 6.9 dB loss) and localized under dark-adapted conditions (up to 15.4 dB loss). Conclusions: Visual field sensitivity losses suggest neural abnormalities in early stage diabetic eye disease and the pattern of the sensitivity losses differed depending on the adaptation conditions. Chromatic perimetry may be useful for subtyping individuals who have mild or no diabetic retinopathy and for better understanding their neural dysfunction.

COMPARATIVE ANALYSIS OF OCT AND OCT ANGIOGRAPHY CHARACTERISTICS IN EARLY DIABETIC RETINOPATHY.

PURPOSE: To assess the quantitative characteristics of optical coherence tomography (OCT) and OCT angiography (OCTA) for the objective detection of early diabetic retinopathy (DR). METHODS: This was a retrospective and cross-sectional study, which was carried out at a tertiary academic practice with a subspecialty. Twenty control participants, 15 people with diabetics without retinopathy (NoDR), and 22 people with mild nonproliferative diabetic retinopathy (NPDR) were included in this study. Quantitative OCT characteristics were derived from the photoreceptor hyperreflective bands, i.e., inner segment ellipsoid (ISe) and retinal pigment epithelium (RPE). OCTA characteristics, including vessel diameter index (VDI), vessel perimeter index (VPI), and vessel skeleton density (VSD), were evaluated. RESULTS: Quantitative OCT analysis indicated that the ISe intensity was significantly trending downward with DR advancement. Comparative OCTA revealed VDI, VPI, and VSD as the most sensitive characteristics of DR. Correlation analysis of OCT and OCTA characteristics revealed weak variable correlation between the two imaging modalities. CONCLUSION: Quantitative OCT and OCTA analyses revealed photoreceptor and vascular distortions in early DR. Comparative analysis revealed that the OCT intensity ratio, ISe/RPE, has the best sensitivity for early DR detection. Weak variable correlation of the OCT and OCTA characteristics suggests that OCT and OCTA are providing supplementary information for DR detection and classification.

Rhegmatogenous retinal detachment surgery: A review.

Rhegmatogenous retinal detachment (RRD) is a serious surgical condition with significant ocular morbidity if not managed properly. Once untreatable, approaches to the repair of RRD have greatly evolved over the years, leading to outstanding primary surgical success rates. The management of RRD is often a topic of great debate. Scleral buckling, vitrectomy and pneumatic retinopexy have been used successfully for the treatment of RRD. Several factors may affect surgical success and dictate a surgeon's preference for the technique employed. In this review, we provide an overview and supporting literature on the options for RRD repair and their respective preoperative and postoperative considerations in order to guide surgical management.

Impact of travel distance on outcomes for clinical trial patients: the Kinghorn Cancer Centre experience.

BACKGROUND: Geographic isolation and travel distance to specialist care is a known social determinant of health and contributes to poorer oncology survival outcomes. AIMS: To compare survival and toxicity outcomes for patients travelling long distances (>50 km) for treatment on clinical trials with local patients (<10 km and 10-50 km). METHODS: We performed a retrospective cohort study based at the Kinghorn Cancer Centre, a comprehensive cancer care centre in metropolitan Sydney. We included adult patients with advanced solid-organ malignancies who were enrolled on therapeutic clinical trials between July 2015 and December 2017. Outcome measures included overall survival, progression-free survival, rates of grade 3-4 toxicity and unplanned hospital admissions for the duration of the clinical trial. RESULTS: We included 173 patients, of whom 27% lived within 10 km, 29% lived between 10 and 50 km and 44% lived further than 50 km. We did not identify significant differences between survival or toxicity outcomes between patients travelling long distances and local patients. CONCLUSIONS: All patients should be considered for clinical trial referral based on clinical parameters and preference, regardless of geographic proximity. In the meantime, improving access to clinical trials for rural and regional patients continues to be a priority.