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Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning

PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age‐related macular degeneration (nAMD). METHODS: In this prospective interventiona...

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Autores principales: Schranz, Markus, Told, Reinhard, Hacker, Valentin, Reiter, Gregor S., Reumueller, Adrian, Vogl, Wolf‐Dieter, Bogunovic, Hrvoje, Sacu, Stefan, Schmidt‐Erfurth, Ursula, Roberts, Philipp K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087766/
https://www.ncbi.nlm.nih.gov/pubmed/35912717
http://dx.doi.org/10.1111/aos.15219
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author Schranz, Markus
Told, Reinhard
Hacker, Valentin
Reiter, Gregor S.
Reumueller, Adrian
Vogl, Wolf‐Dieter
Bogunovic, Hrvoje
Sacu, Stefan
Schmidt‐Erfurth, Ursula
Roberts, Philipp K.
author_facet Schranz, Markus
Told, Reinhard
Hacker, Valentin
Reiter, Gregor S.
Reumueller, Adrian
Vogl, Wolf‐Dieter
Bogunovic, Hrvoje
Sacu, Stefan
Schmidt‐Erfurth, Ursula
Roberts, Philipp K.
author_sort Schranz, Markus
collection PubMed
description PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age‐related macular degeneration (nAMD). METHODS: In this prospective interventional study, 54 patients with treatment‐naïve type 1 or 2 nAMD were included and treated with intravitreal aflibercept. At baseline and month 1, each patient underwent a SD‐OCT volume scan and volumetric flow scan using a swept‐source OCTA. A deep learning algorithm was used to automatically detect and quantify fluid in OCT scans. Angio Tool, a National Cancer Institute algorithm, was used to skeletonize MNV properties and quantify lesion size (LS), vessel area (VA), vessel density (VD), total number of endpoints (TNE), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL) and mean‐e‐lacunarity (MEL). Subsequently, linear regression models were used to investigate a correlation between OCTA parameters and fluid quantifications. RESULTS: The median amount of fluid within the central 6‐mm EDTRS ring was 173.7 nl at baseline, consisting of 156.6 nl of subretinal fluid (SRF) and 2.3 nl of intraretinal fluid (IRF). Fluid decreased significantly in all compartments to 1.76 nl (SRF) and 0.64 nl (IRF). The investigated MNV parameters did not change significantly after the first treatment. There was no significant correlation between MNV parameters and relative fluid decrease after anti‐VEGF treatment. Baseline fluid correlated statistically significant but weakly with TNE (p = 0.002, R (2) = 0.17), SRF with TVL (p = 0.04, R (2) = 0.08), VD (p = 0.046, R (2) = 0.08), TNE (p = 0.001, R (2) = 0.20) and LS (p = 0.033, R (2) = 0.09). IRF correlated with VA (p = 0.042, R (2) = 0.08).The amount of IRF at month 1 correlated significantly but weakly with VD (p = 0.036, R (2) = 0.08), JD (p = 0.019, R (2) = 0.10) and MEL (p = 0.005, R (2) = 0.14). CONCLUSION: Macular neovascularization parameters at baseline and month 1 played only a minor role in the exudation process in nAMD. None of the MNV parameters were correlated with the treatment response.
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spelling pubmed-100877662023-04-12 Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning Schranz, Markus Told, Reinhard Hacker, Valentin Reiter, Gregor S. Reumueller, Adrian Vogl, Wolf‐Dieter Bogunovic, Hrvoje Sacu, Stefan Schmidt‐Erfurth, Ursula Roberts, Philipp K. Acta Ophthalmol Original Articles PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age‐related macular degeneration (nAMD). METHODS: In this prospective interventional study, 54 patients with treatment‐naïve type 1 or 2 nAMD were included and treated with intravitreal aflibercept. At baseline and month 1, each patient underwent a SD‐OCT volume scan and volumetric flow scan using a swept‐source OCTA. A deep learning algorithm was used to automatically detect and quantify fluid in OCT scans. Angio Tool, a National Cancer Institute algorithm, was used to skeletonize MNV properties and quantify lesion size (LS), vessel area (VA), vessel density (VD), total number of endpoints (TNE), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL) and mean‐e‐lacunarity (MEL). Subsequently, linear regression models were used to investigate a correlation between OCTA parameters and fluid quantifications. RESULTS: The median amount of fluid within the central 6‐mm EDTRS ring was 173.7 nl at baseline, consisting of 156.6 nl of subretinal fluid (SRF) and 2.3 nl of intraretinal fluid (IRF). Fluid decreased significantly in all compartments to 1.76 nl (SRF) and 0.64 nl (IRF). The investigated MNV parameters did not change significantly after the first treatment. There was no significant correlation between MNV parameters and relative fluid decrease after anti‐VEGF treatment. Baseline fluid correlated statistically significant but weakly with TNE (p = 0.002, R (2) = 0.17), SRF with TVL (p = 0.04, R (2) = 0.08), VD (p = 0.046, R (2) = 0.08), TNE (p = 0.001, R (2) = 0.20) and LS (p = 0.033, R (2) = 0.09). IRF correlated with VA (p = 0.042, R (2) = 0.08).The amount of IRF at month 1 correlated significantly but weakly with VD (p = 0.036, R (2) = 0.08), JD (p = 0.019, R (2) = 0.10) and MEL (p = 0.005, R (2) = 0.14). CONCLUSION: Macular neovascularization parameters at baseline and month 1 played only a minor role in the exudation process in nAMD. None of the MNV parameters were correlated with the treatment response. John Wiley and Sons Inc. 2022-08-01 2023-02 /pmc/articles/PMC10087766/ /pubmed/35912717 http://dx.doi.org/10.1111/aos.15219 Text en © 2022 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Schranz, Markus
Told, Reinhard
Hacker, Valentin
Reiter, Gregor S.
Reumueller, Adrian
Vogl, Wolf‐Dieter
Bogunovic, Hrvoje
Sacu, Stefan
Schmidt‐Erfurth, Ursula
Roberts, Philipp K.
Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
title Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
title_full Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
title_fullStr Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
title_full_unstemmed Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
title_short Correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
title_sort correlation of vascular and fluid‐related parameters in neovascular age‐related macular degeneration using deep learning
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087766/
https://www.ncbi.nlm.nih.gov/pubmed/35912717
http://dx.doi.org/10.1111/aos.15219
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