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M. Hangai, A. Sakamoto, S. Mori, H. Nakanishi, Y. Kotera, R. Inoue, S. Morishita, F. Hirose, T. Ojima, N. Yoshimura; Macular Retinal Nerve Fiber Layer Segmentation by 3-Dimensional Spectral Domain Optical Coherence Tomography in Glaucoma Diagnosis. Invest. Ophthalmol. Vis. Sci. 2008;49(13):4652.
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© ARVO (1962-2015); The Authors (2016-present)
To test the efficacy of the three-dimensional (3D) volume of macular retinal nerve fiber layer (mRNFL) generated by automated segmentation in spectral domain optical coherence tomography (SD-OCT) in detecting mRNFL defects and discriminating glaucomatous from normal eyes.
An observational cross-sectional, case-control, comparative study using 3D OCT-1000 (Topcon, Japan) was performed in 85 eyes (85 subjects), including 46 normal and 39 glaucomatous eyes. Raster scan protocol of 512 × 128 scans covering a 6 × 6 mm square macular area was used to obtain a single 3D data set. Automated segmentation of the RNFL and whole retina was performed on the 3D data set by using the software developed by Topcon Medical Systems, Inc. The mean thicknesses of the mRNFL and total retina were shown by color-coded mapping. mRNFL and total retina thicknesses obtained by 3D-OCT were compared with those obtained by Stratus OCT fast circumpapillary RNFL (cpRNFL) scan and fast macular thickness mapping to determine their correlation with the mean deviation (MD) of static automated perimetry. The area under receiver operating characteristic curve (AROC) was calculated for each measurement to distinguish between normal and glaucomatous eyes.
The color-coded map of mRNFL thickness clearly depicted RNFL defects in the macular regions that corresponded well with the RNFL defects detected in the color fundus and red-free photographs. It also clearly depicted the preperimetric RNFL changes such as very narrow RNFL defects and RNFL thinning, which were not detected by Stratus OCT fast cpRNFL thickness scan. Compared to the normal group, the glaucoma group exhibited the highest thinning ratio in the mRNFL mean thickness measured using SD-OCT (thinning by 41.3%), followed by cpRNFL by Stratus OCT (by 29.0%) and macular retina thickness by SD-OCT (8.3%) and Stratus OCT (8.1%); all these parameters significantly correlated with the mean deviation of static automated perimetry (r = 0.691, 0.768, 0.633, and 0.618, respectively; P < 0.01, Pearson’s correlation). The AROCs for mRNFL thickness by SD-OCT, macular retina thicknesses by SD-OCT and Stratus OCT, and cpRNFL thickness by Stratus OCT for discriminating glaucomatous from normal eyes were 0.901, 0.877, 0.810, and 0.916, respectively.
Thus, macular RNFL volume obtained by 3D SD-OCT is a sensitive indicator of early mRNFL changes in glaucoma and can be potentially useful in discriminating glaucomatous from normal eyes for glaucoma diagnosis.
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