Computational protocol: Volume-Rendered Projection-Resolved OCT Angiography: 3D Lesion Complexity Is Associated With Therapy Response in Wet Age-Related Macular Degeneration

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Protocol publication

[…] Two masked, independent graders (PLN and ADT) used cross-sectional PR-OCTA to evaluate two cross sections with the highest CNV flow signal. To standardize the approach, a senior grader identified and exported the two B-scans with the highest apparent CNV flow signal from within the entire imaging volume. Then, each masked grader independently assessed the same B-scan for highest CNV flow signal. “Highest CNV flow signal” was defined as the most anterior point of CNV flow signal (decorrelation signal), or in other words, the CNV flow signal farthest from Bruch's membrane. Images were exported into ImageJ (http://imagej.nih.gov/ij/; provided in the public domain by the National Institutes of Health, Bethesda, MD, USA). The Line Tool was used to measure the distance between Bruch's membrane and the highest point of CNV flow signal. The greater of the two measurements for each grader was recorded for each eye.Each PR-OCTA volume was then assessed for two additional parameters: the number of CNV flow layers and the CNV flow signal thickness (). “Number of CNV flow layers” was defined as the number of layers of pathological decorrelation signal that were separated by at least 30 μm in the axial direction (). CNV flow layers that were separated by less than 30 μm were counted as a single CNV flow layer because the axial resolution of the device is 15 μm at best. To ensure that CNV flow layers were not projection artifact, we evaluated the intensity of the decorrelation signal using the Plot Profile tool in ImageJ (). In CNVs with more than one flow layer, we also measured the “CNV flow signal thickness,” defined as the distance between the most distal CNV flow layers. For eyes with a single CNV flow layer, this value was recorded as zero. Highest CNV flow signal, number of CNV flow layers, and CNV flow signal thickness were averaged between the two graders and recorded for each eye. shows examples of the CNV flow signal appreciated within thick CNV lesions and CNV lesions underlying subretinal fluid or subretinal hyperreflective material.Intraretinal hyperreflective foci have been identified as important precursors of nAMD progression.– We therefore evaluated the structural OCT volume for hyperreflective foci to assess whether their presence was associated with treatment response. [...] We performed statistical tests with SPSS version 21 (IBM SPSS Statistics; IBM Corporation, Chicago, IL, USA). Independent samples t tests were used to compare PR-OCTA parameters, as well as demographic characteristics for the following groups: (1) all subjects: good versus poor responders, (2) short-term imaging: good versus poor responders; and (3) long-term imaging: good versus poor responders. Two-way random intraclass correlation coefficients (ICCs) were used to assess reliability for PR-OCTA parameters. Shapiro-Wilk test was significant, indicating the data deviated from normal distribution. Levene's test for equality of variances was not significant, indicating homoscedasticity. Therefore, we performed nonparametric Spearman rank correlations to explore the relationships between VA and linear CNV complexity parameters (CNV flow signal thickness and highest CNV flow signal). Because “number of CNV flow layers” is a categorical variable, we used Kruskal-Wallis H Test with post hoc Dunn test to compare continuous variables (highest CNV flow signal with PR and VA) based on the number of CNV flow layers. Because CNVs with a single flow layer have a CNV flow thickness of zero, we compared only the CNV flow thickness between CNV with two and three flow layers using an independent samples t-test. We also used independent samples t tests to compare the means of continuous variables (highest CNV flow signal with PR, CNV flow signal thickness, and VA) based on hyperreflective foci (present or absent). A P value of less than 0.05 was considered statistically significant. […]

Pipeline specifications

Software tools ImageJ, SPSS
Applications Miscellaneous, Microscopic phenotype analysis
Organisms Homo sapiens
Diseases Eye Abnormalities, Macular Degeneration