Purpose
To quantitatively assess the depth of retinal nerve fiber layer (RNFL) defects using a Cirrus high-definition (HD)-optical coherence tomography (OCT)-derived RNFL thickness deviation map.
Methods
For the 315 glaucomatous eyes with localized and diffuse RNFL defects, the severity of the RNFL defect was graded on red-free fundus photographs by two observers using a standardized protocol with a 3-level grading system. The RNFL defect depth on the RNFL thickness deviation map was expressed as an RNFL defect depth percentage index (RDPI): 100x[1-{summation of thicknesses of RNFL defects (red or yellow superpixels)/summation of RNFL thicknesses of upper 95th percentile range of 217 eyes of age-matched healthy subjects in areas corresponding to RNFL defects}]. RDPI, average and segmental (four quadrants and 12 clock-hour sectors) circumpapillary RNFL (cpRNFL) thicknesses according to the RNFL defect severity were derived, and the Area under the receiver operating characteristic curves (AUCs) for various OCT parameters were compared.
Results
The RDPIs increased with the increasing severity of the RNFL defect in both the superior and inferior hemifields (P < 0.05, one-way ANOVA testwith Bonferroni’s correction). The AUCs of the RDPIs (0.969 and 0.975 in the superior and inferior hemifields, respectively) were larger than those of all of the cpRNFL thicknesses in discriminating mild and moderate RNFL defects (P < 0.05). Meanwhile, in discriminating moderate and severe RNFL defects, the AUCs of the RDPIs (0.961 and 0.891 in the superior and inferior hemifields, respectively) were larger than those of the cpRNFL thicknesses in all areas except the inferior quadrant, 6, 7, and 11 o’clock sectors (P < 0.05).
Conclusions
RDPI, a new parameter utilizing a Cirrus HD-OCT-derived RNFL thickness deviation map, can be a useful adjunct tool for objective quantification of RNFL defect depth. Furthermore, this parameter has an advantage over cpRNFL thickness, especially in discriminating between mild and moderate RNFL defects.
Keywords: 550 imaging/image analysis: clinical