Imperial College London
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Professor M Francesca Cordeiro

Faculty of Medicine, Department of Surgery & Cancer

Chair in Ophthalmology (Clinical)
 
 
 
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Contact

 

m.cordeiro

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yap:2022:10.1016/j.ogla.2022.06.002,
author = {Yap, TE and Davis, BM and Bloom, PA and Cordeiro, MF and Normando, EM},
doi = {10.1016/j.ogla.2022.06.002},
journal = {Ophthalmology Glaucoma},
pages = {562--571},
title = {Glaucoma Rose Plot Analysis: detecting early structural progression using angular histograms},
url = {http://dx.doi.org/10.1016/j.ogla.2022.06.002},
volume = {5},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeTo evaluate the novel Rose Plot Analysis (RPA) in the analysis and presentation of glaucoma structural progression data.DesignCase-control image analysis study using retrospective retinal imaging series.SubjectsSubjects with open-angle glaucoma with at least 5 registered spectral-domain OCT scans.MethodsGlaucoma RPA was developed, combining a novel application of angular histograms and dynamic cluster analysis of circumpapillary retinal nerve fiber layer (cRNFL) OCT data. Rose Plot Analysis plots were created for each eye and each visit. Significant clusters of progression were indicated in red. Three masked clinicians categorized all RPA plots (progressing, not progressing), in addition to measuring the significant RPA area. A masked OCT series assessment with linear regression of averaged global and sectoral cRNFL thicknesses was conducted as the clinical imaging standard.Main Outcome MeasuresInterobserver agreement was compared between RPA and the clinical imaging standard. Discriminative ability was assessed using receiver-operating characteristic curves. The time to detection of progression was compared using a Kaplan–Meier survival analysis, and the agreement of RPA with the clinical imaging standard was calculated.ResultsSeven hundred fourty-three scans from 98 eyes were included. Interobserver agreement was significantly greater when categorizing RPA (κ, 0.86; 95% confidence interval [CI], 0.81–0.91) compared with OCT image series (κ, 0.66; 95% CI, 0.54–0.77). The discriminative power of RPA to differentiate between eyes that were progressing and not progressing (area under the curve [AUC], 0.97; 95% CI, 0.92–1.00) was greater than that of global cRNFL thickness (AUC, 0.71; 95% CI, 0.59–0.82; P < 0.0001) and equivalent to that of sectoral cRNFL regression (AUC, 0.97; 95% CI, 0.92–1.00). A Kaplan–Meier survival analysis showed that progression was detected 8.7 months sooner by RPA than by global
AU  - Yap,TE
AU  - Davis,BM
AU  - Bloom,PA
AU  - Cordeiro,MF
AU  - Normando,EM
DO  - 10.1016/j.ogla.2022.06.002
EP  - 571
PY  - 2022///
SN  - 2589-4196
SP  - 562
TI  - Glaucoma Rose Plot Analysis: detecting early structural progression using angular histograms
T2  - Ophthalmology Glaucoma
UR  - http://dx.doi.org/10.1016/j.ogla.2022.06.002
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000893576600003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.sciencedirect.com/science/article/pii/S2589419622000953
UR  - http://hdl.handle.net/10044/1/104395
VL  - 5
ER  -
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