Safety of Intravitreal Injection of Stivant, A Biosimilar to Bevacizumab, in Rabbit Eyes

Purpose: To evaluate the safety of intravitreal injection of Stivant, a biosimilar to bevacizumab, in rabbits using electrophysiological and histological analysis. Methods: Both eyes of 41 New Zealand albino rabbits were injected with 0.1 mL (2.5 mg) of Stivant. The rabbits were scheduled to be sacrificed 1, 2, 7, 14, and 28 days after injection for histopathological evaluations. Clinical examinations and electroretinography (ERG) were performed at baseline and just before sacrificing the rabbits. Fourteen separate rabbits received a reference drug (Avastin) and were considered as the control group. Furthermore, three other rabbits received the same volume of saline (saline control group). Rabbits of both control groups were sacrificed four weeks after injection. ERG was performed 1, 2, 7, 14, and 28 days after injections. Results: No significant difference was observed in a- and b-wave amplitudes and latency after intravitreal Stivant injection between baseline and different time points. Moreover, there was no statistically significant difference in wave amplitudes and latency between the Stivant and control groups. The histology of rabbit eyes of the Stivant and control groups after intravitreal injections was not distinguishable. Conclusion: The biosimilar Stivant, up to a dose of 2.5 mg, did not appear to be toxic to the retina in albino rabbits. These results suggest that this drug could be a safe and inexpensive alternative to intravitreal bevacizumab. The efficacy of these injections was not investigated in this study and needs to be evaluated in future studies

CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA)

This paper aims to present an artificial intelligence-based algorithm for the automated segmentation of Choroidal Neovascularization (CNV) areas and to identify the presence or absence of CNV activity criteria (branching, peripheral arcade, dark halo, shape, loop and anastomoses) in OCTA images. Methods: This retrospective and cross-sectional study includes 130 OCTA images from 101 patients with treatment-naïve CNV. At baseline, OCTA volumes of 6 × 6 mm2 were obtained to develop an AI-based algorithm to evaluate the CNV activity based on five activity criteria, including tiny branching vessels, anastomoses and loops, peripheral arcades, and perilesional hypointense halos. The proposed algorithm comprises two steps. The first block includes the pre-processing and segmentation of CNVs in OCTA images using a modified U-Net network. The second block consists of five binary classification networks, each implemented with various models from scratch, and using transfer learning from pre-trained networks. Results: The proposed segmentation network yielded an averaged Dice coefficient of 0.86. The individual classifiers corresponding to the five activity criteria (branch, peripheral arcade, dark halo, shape, loop, and anastomoses) showed accuracies of 0.84, 0.81, 0.86, 0.85, and 0.82, respectively. The AI-based algorithm potentially allows the reliable detection and segmentation of CNV from OCTA alone, without the need for imaging with contrast agents. The evaluation of the activity criteria in CNV lesions obtains acceptable results, and this algorithm could enable the objective, repeatable assessment of CNV features