Imaging Biomarkers of 1-Year Activity in Type 1 Macular Neovascularization.
Journal
Translational vision science & technology
ISSN: 2164-2591
Titre abrégé: Transl Vis Sci Technol
Pays: United States
ID NLM: 101595919
Informations de publication
Date de publication:
03 05 2021
03 05 2021
Historique:
entrez:
10
6
2021
pubmed:
11
6
2021
medline:
29
6
2021
Statut:
ppublish
Résumé
The purpose of this study was to evaluate the predictive value of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters at baseline on lesion's activity at the 1-year follow-up in type 1 macular neovascularizations (MNVs) treated with 1-year fixed regimen of intravitreal aflibercept injections (q8 IAIs). All patients were imaged by structural OCT to evaluate central macular thickness (CMT), subretinal fluid (SRF), subretinal hyper-reflective material (SHRM), intraretinal fluid (IRF) and intraretinal hyper-reflective dots (HRDs), and by Swept-Source OCTA to measure baseline MNV area, perfusion density (PD), vessel length density (VLD), and vessel diameter index. At the end of q8 IAI, patients were classified in two groups: active-MNV (A-MNV) and inactive-MNV (I-MNV), considering the OCT signs of activity. Three binary logistic regression models were developed: (1) OCT-based, (2) OCTA-based, and (3) OCT/OCTA-based model. Thirty-one treatment-naïve type 1 MNVs were enrolled (13 A-MNV and 18 I-MNV). No differences were observed in baseline OCT and OCTA characteristics between A-MNV and I-MNV. Among the models developed, model 3 that combined OCT/OCTA parameters showed a performance of 87.5% and excellent sensitivity for A-MNV lesions (100%). By analyzing the model, the A-MNV group appears more likely to show at baseline SRF, greater CMT, wider MNV area, and lower PD and VLD compared to I-MNV. Our study demonstrated that the combination of baseline OCT and OCTA parameters allowed to achieve a good models' performance in the prediction of MNV activity permitting to correctly classifying the active lesions at the end of follow-up period, with excellent sensitivity. OCT/OCTA could integrate statistical models potentially useful for artificial intelligence.
Identifiants
pubmed: 34111264
pii: 2772591
doi: 10.1167/tvst.10.6.18
pmc: PMC8131998
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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