Intraretinal Hyper-Reflective Foci Are Almost Universally Present and Co-Localize With Intraretinal Fluid in Diabetic Macular Edema.

Purpose: In diabetic macular edema (DME), hyper-reflective foci (HRF) has been linked to disease severity and progression. Using an automated approach, we aimed to investigate the baseline distribution of HRF in DME and their co-localization with cystoid intraretinal fluid (IRF). Methods: Baseline spectral-domain optical coherence tomography (SD-OCT) volume scans (N = 1527) from phase III clinical trials YOSEMITE (NCT03622580) and RHINE (NCT03622593) were segmented using a deep-learning-based algorithm (developed using B-scans from BOULEVARD NCT02699450) to detect HRF. The HRF count and volume were assessed. HRF distributions were analyzed in relation to best-corrected visual acuity (BCVA), central subfield thickness (CST), and IRF volume in quartiles, and Diabetic Retinopathy Severity Scores (DRSS) in groups. Co-localization of HRF with IRF was calculated in the central 3-mm diameter using the en face projection. Results: HRF were present in most patients (up to 99.7%). Median (interquartile range [IQR]) HRF volume within the 3-mm diameter Early Treatment Diabetic Retinopathy Study ring was 1964.3 (3325.2) pL, and median count was 64.0 (IQR = 96.0). Median HRF volumes were greater with decreasing BCVA (nominal P = 0.0109), and increasing CST (nominal P < 0.0001), IRF (nominal P < 0.0001), and DRSS up to very severe nonproliferative diabetic retinopathy (nominal P < 0.0001). HRF co-localized with IRF in the en face projection. Conclusions: Using automated HRF segmentation of full SD-OCT volumes, we observed that HRF are a ubiquitous feature in DME and exhibit relationships with BCVA, CST, IRF, and DRSS, supporting a potential link to disease severity. The spatial distribution of HRF closely followed that of IRF.

Anti-VEGF-resistant subretinal fluid is associated with better vision and reduced risk of macular atrophy.

BACKGROUND/AIM: To evaluate relationships between subretinal fluid (SRF), macular atrophy (MA) and visual outcomes in ranibizumab-treated neovascular age-related macular degeneration (nAMD). METHODS: This post hoc HARBOR trial (NCT00891735) analysis included ranibizumab-treated (0.5 or 2.0 mg, monthly or as-needed, all treatment arms pooled) eyes with nAMD and baseline (screening, baseline and week 1) SRF. SRF presence, SRF thickness (0, >0-50, >50-100 and >100 µm) and subretinal fluid volume (SRFV) were determined by spectral domain optical coherence tomography (SD-OCT). Best-corrected visual acuity (BCVA) was assessed. MA was identified using fluorescein angiograms and colour fundus photographs, as well as SD-OCT. RESULTS: Seven hundred eighty-five of 1097 eyes met analysis criteria. In eyes without baseline MA, residual versus no SRF at month (M) 3 was associated with lower MA rates at M12 (5.1% vs 22.1%) and M24 (13.3% vs 31.2%) (both p<0.0001); MA percentages at M12/M24 were similar among patients with residual SRF at M6. Higher baseline SRFV was associated with a lower MA rate. Greater mean BCVA was observed with residual SRF of any thickness (>0-50 µm, 71.2 letters; >50-100 µm, 71.3 letters; >100 µm, 69.2 letters) versus no SRF (63.6 letters), but the change in BCVA from baseline to M12 or M24 was the same for eyes with or without treatment-resistant subretinal fluid (TR-SRF) at M3 or M6. CONCLUSION: TR-SRF was not detrimental to vision outcomes over 2 years, regardless of thickness. MA rates were significantly higher without TR-SRF.

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

Purpose: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs). Methods: Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 µm and 400 µm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs. Results: The best DL model was able to predict CST ≥ 250 µm and CFT ≥ 250 µm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ≥ 400 µm and CFT ≥ 400 µm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars. Conclusions: DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.

Automated and reproducible read-across like models for predicting carcinogenic potency.

Several qualitative (hazard-based) models for chronic toxicity prediction are available through commercial and freely available software, but in the context of risk assessment a quantitative value is mandatory in order to be able to apply a Margin of Exposure (predicted toxicity/exposure estimate) approach to interpret the data. Recently quantitative models for the prediction of the carcinogenic potency have been developed, opening some hopes in this area, but this promising approach is currently limited by the fact that the proposed programs are neither publically nor commercially available. In this article we describe how two models (one for mouse and one for rat) for the carcinogenic potency (TD50) prediction have been developed, using lazar (Lazy Structure Activity Relationships), a procedure similar to read-across, but automated and reproducible. The models obtained have been compared with the recently published ones, resulting in a similar performance. Our aim is also to make the models freely available in the near future thought a user friendly internet web site.