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Updated: Jul 18, 2026
In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
Published on: January 21, 2018
Quantitative fluorescein angiographic analysis of choroidal neovascular membranes: validation and correlation with
Usha Chakravarthy1, Alexander C Walsh, Alyson Muldrew
1Doheny Retina Institute Advanced Macular Diagnostics Lab, Doheny Image Reading Center, Doheny Eye Institute, Keck School of Medicine, at the University of Southern California, Los Angeles, California 90033, USA.
Purpose:
To compare computerized analysis with traditional grading methods in the analysis of fluorescein angiograms from patients with choroidal neovascularization (CNV) due to age-related macular degeneration (AMD) and to examine the clinical relevance of parameters generated by computerized analysis by testing their relationships with clinical measures of vision.
Methods:
Custom quantitative fluorescein analysis (QFA) software was used to analyze 62 angiograms from patients with CNV for whom distance visual acuity (DVA) data were available. On applying QFA, we obtained three mathematical parameters for each lesion component: pixel area (PA), integrated intensity (II), and positive fluorescence (PF). Quotients (Q) were derived for the latter two parameters by correcting against background (b) or optic nerve (o) fluorescence (IIQ(b), IIQ degrees , PFQ(b), and PFQ degrees ). The new metrics were compared with traditional grading parameters of classic CNV and lesion area. The relationships of both sets of angiographic data with measures of vision were explored by regression analyses.
Results:
Weighted kappa between QFA and traditional grading for lesion subtype assessment was high (kappa = 0.7). Regression analyses with PA, IIQ(b), IIQ degrees , PFQ(b), and PFQ degrees for each lesion descriptor (leakage, classic CNV, occult CNV, total lesion) as independent variables and DVA as the dependent variable showed that in every case PFQ(b) exhibited the most significant relationship with vision (adjusted r(2) = 0.26). Parameter estimates showed that for a change of 30 units on the PFQ(b) for classic CNV, a loss of 20 letters of DVA may be expected. No parameters from traditional grading methods showed statistically significant relationships with DVA.
Conclusions:
The markers of dynamic change in area and intensity of fluorescence exhibited stronger relationships with visual function than did area measurements alone.
