July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Retinal Nerve Fiber Layer (RNFL) Optical Texture Analysis (ROTA) for Evaluation of RNFL Abnormalities in Glaucoma
Author Affiliations & Notes
  • Christopher Kai-Shun Leung
    3/F, University Eye Center, The Chinese University of Hong Kong, Hong Kong, Hong Kong
  • Footnotes
    Commercial Relationships   Christopher Leung, Carl Zeiss Meditec (F), Heidelberg Engineering (F), Tomey (F), Tomey (R)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 3497. doi:
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    • Get Citation

      Christopher Kai-Shun Leung; Retinal Nerve Fiber Layer (RNFL) Optical Texture Analysis (ROTA) for Evaluation of RNFL Abnormalities in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3497.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Determination of RNFL abnormalities in optical coherence tomography (OCT) is typically relied on normative databases. We have devised a novel algorithm, ROTA, integrating RNFL thickness and RNFL reflectance data obtained from a swept-source OCT for wide-field detection and quantification of RNFL abnormalities independent of normative databases. This study compared the diagnostic performance and structure function association between ROTA and conventional RNFL thickness analysis for evaluation of glaucoma.

Methods : 72 glaucoma patients (mean (SD) age: 54.3(16.0) years; visual field (VF) MD: -5.8(6.3) dB) and 54 normal individuals (mean age: 63.7(4.9) years) had wide-field OCT RNFL imaging (12x9mm2, Triton OCT, Japan) and perimetry. RNFL optical density data (512x256 pixels) were exported for ROTA, which calculates pixel by pixel optical texture signature values (Fig.1). Conventional RNFL and GCIPL thickness analyses was performed using the Cirrus HD-OCT (Carl Zeiss Meditec, USA).

Results : ROTA reveals localized RNFL abnormalities that would be missed by conventional RNFL thickness analysis (Fig.2A), and discerns different levels of RNFL damage in advanced glaucoma that would not be feasible with conventional RNFL thickness analysis (Fig.2B&C). Defining glaucoma as having glaucomatous VF defects, the specificity and sensitivity of ROTA to detect RNFL abnormalities in glaucoma were 98.1% (95% CI: 90.1-100.0%) and 95.8% (88.3-99.1%), respectively. Compared with ROTA, combined parapapillary RNFL thickness (6x6mm2) and macular GCIPL thickness (6x6mm2) analysis (≥20 pixels encoded in red in the RNFL/GCIPL thickness deviation map) showed a similar sensitivity (98.6%, 92.5-99.9%) but a significantly lower specificity (79.6%, 66.4-89.3%; p=0.002). The association between average RNFL optical texture signature value and VF MD was significantly stronger (R2=0.25) compared with the association between average RNFL thickness and VF MD (R2=0.12) (p<0.001).

Conclusions : ROTA reveals optical textural details of the RNFL that are not discernible in the conventional RNFL thickness map. ROTA outperforms RNFL/GCIPL thickness analysis for evaluation of RNFL abnormalities in glaucoma.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

Algorithm for calculation of RNFL optical texture signature values in ROTA

Algorithm for calculation of RNFL optical texture signature values in ROTA

 

ROTA of an eye with early glaucoma (A) and eyes with advanced glaucoma (B&C)

ROTA of an eye with early glaucoma (A) and eyes with advanced glaucoma (B&C)

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