Comparison of methods to quantify macular and peripapillary vessel density in optical coherence tomography angiography

To compare macular and peripapillary vessel density values calculated on optical coherence tomography angiography (OCT-A) images with different algorithms, elaborate conversion formula, and compare the ability to discriminate healthy from affected eyes. Cross-sectional study of healthy subjects, pat...

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Bibliographic Details
Published in:PloS one Vol. 13; no. 10; p. e0205773
Main Authors: Rabiolo, Alessandro, Gelormini, Francesco, Sacconi, Riccardo, Cicinelli, Maria Vittoria, Triolo, Giacinto, Bettin, Paolo, Nouri-Mahdavi, Kouros, Bandello, Francesco, Querques, Giuseppe
Format: Journal Article
Language:English
Published: United States Public Library of Science 18.10.2018
Public Library of Science (PLoS)
Subjects:
Adult
Aged
Algorithms
Angiography
Biology and Life Sciences
Capillaries - diagnostic imaging
Care and treatment
Comparative analysis
Computed Tomography Angiography - methods
Conversion
Cross-Sectional Studies
Density
Diabetes
Diabetes mellitus
Diabetic retinopathy
Diabetic Retinopathy - diagnostic imaging
Diagnosis
Diagnostic systems
Eye
Eye (anatomy)
Female
Glaucoma
Glaucoma - diagnostic imaging
Healthy Volunteers
Humans
Image Processing, Computer-Assisted - methods
Macula Lutea - blood supply
Macula Lutea - diagnostic imaging
Male
Mathematical analysis
Medical imaging
Medicine and Health Sciences
Methods
Middle Aged
Optical Coherence Tomography
Optical tomography
Optics
Patients
Physical Sciences
Quantitative analysis
Reliability
Reproducibility of Results
Research and Analysis Methods
Retina
Retinal Vessels - diagnostic imaging
Retinopathy
Studies
Tomography
Tomography, Optical Coherence - methods
Italy
Los Angeles California
California
United States > US
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Description
Summary:To compare macular and peripapillary vessel density values calculated on optical coherence tomography angiography (OCT-A) images with different algorithms, elaborate conversion formula, and compare the ability to discriminate healthy from affected eyes. Cross-sectional study of healthy subjects, patients with diabetic retinopathy, and glaucoma patients (44 eyes in each group). Vessel density in the macular superficial capillary plexus (SCP), deep capillary plexus (DCP), and the peripapillary radial capillary plexus (RCP) were calculated with seven previously published algorithms. Systemic differences, diagnostic properties, reliability, and agreement of the methods were investigated. Healthy eyes exhibited higher vessel density values in all plexuses compared to diseased eyes regardless of the algorithm used (p<0.01). The estimated vessel densities were significantly different at all the plexuses (p<0.0001) as a function of method used. Inter-method reliability and agreement was mostly poor to moderate. A conversion formula was available for every method, except for the conversion between multilevel and fixed at the DCP. Substantial systemic, non-constant biases were evident between many algorithms. No algorithm outperformed the others for discrimination of patients from healthy subjects in all the retinal plexuses, but the best performing algorithm varied with the selected plexus. Absolute vessel density values calculated with different algorithms are not directly interchangeable. Differences between healthy and affected eyes could be appreciated with all methods with different discriminatory abilities as a function of the plexus analyzed. Longitudinal monitoring of vessel density should be performed with the same algorithm. Studies adopting vessel density as an outcome measure should not rely on external normative databases.
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Competing Interests: Kouros Nouri-Mahdavi: Heidelberg Engineering (F,R). Giuseppe Querques is consultant for: Alimera Sciences, Allergan Inc, Bayer Shering-Pharma, Fidia-Sooft, Heidelberg, KHB, Lumithera, Novartis, Roche, Sandoz (Berlin, Germany), Topcon, Zeiss. Francesco Bandello has the following disclosures: Allergan (S), Alimera (S), Bayer (S), Farmila-Thea (S), Schering Pharma (S), Sanofi-Aventis (S), Novagali (S), Pharma (S), Hoffmann-La Roche (S), Genentech (S), Novartis (S). This does not alter our adherence to PLOS ONE policies on sharing data and material.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0205773