Comparison of swept-source versus spectral-domain optical coherence tomography angiography for detection of macular neovascularization

Purpose To compare swept-source (SS) versus spectral-domain (SD) optical coherence tomography angiography (OCTA) for the detection of macular neovascularization (MNV). Methods In this prospective cohort study, 72 eyes of 54 patients with subretinal hyperreflective material (SHRM) and/or pigment epithelial detachment (PED) on OCT possibly corresponding to MNV in at least one eye were included. OCTA scans were acquired using two devices, the PLEX Elite 9000 SS-OCTA and the Spectralis SD-OCTA. Fluorescein angiography (FA) was used as reference. Two graders independently evaluated en face OCTA images using a preset slab as well as a manually modified slab, followed by a combination of en face and cross-sectional OCTA. Results Sensitivity (specificity) for the automated slabs was 51.7% (93.0%) for SS-OCTA versus 58.6% (95.3%) for SD-OCTA. Manual modification of segmentation increased sensitivity to 79.3% for SS-OCTA but not for SD-OCTA (58.6%). The combination of en face OCTA with cross-sectional OCTA reached highest sensitivity values (SS-OCTA: 82.8%, SD-OCTA: 86.2%), and lowest number of cases with discrepancies between SS-OCTA and SD-OCTA (4.2%). Fleiss kappa as measure of concordance between FA, SS-OCTA, and SD-OCTA was 0.56 for the automated slabs, 0.60 for the manual slabs, and 0.73 (good agreement) for the combination of en face OCTA with cross-sectional OCTA. Concordance to FA was moderate for the automated slabs and good for manual slabs and combination with cross-sectional OCTA of both devices. Conclusion Both devices reached comparable results regarding the detection of MNV on OCTA. Sensitivity for MNV detection and agreement between devices was best when evaluating a combination of en face and cross-sectional OCTA.

Automated summary

This study compared swept-source (SS) and spectral-domain (SD) optical coherence tomography angiography (OCTA) for detecting macular neovascularization (MNV). The results showed that the sensitivity for MNV detection and agreement between devices was best when evaluating a combination of en face and cross-sectional OCTA.