Contrast-to-Noise Ratios to Evaluate the Detection of Glaucomatous Progression in the Superior and Inferior Hemiretina

Abstract

Purpose: To determine the sensitivity of optical coherence tomography (OCT) and standard automated perimetry (SAP) for detecting glaucomatous progression in the superior and inferior hemiretina.

Methods: We calculated contrast-to-noise ratios (CNRs) for OCT retinal nerve fiber layer (RNFL) thickness of hemiretinas and for SAP mean total deviation (MTD) of the corresponding hemifields from longitudinal data (205 eyes, 125 participants). The glaucoma stage for each hemiretina was based on the corresponding hemifield's MTD. Contrast was defined as the difference of the parameter between two consecutive glaucoma stages, whereas noise was the measurement variability of the parameter in those stages. The higher the CNR of a parameter, the more sensitive it is to detecting progression in the transition between successive stages.

Results: There were no statistically significant differences for the RNFL CNR and MTD CNR between superior and inferior hemiretinas. As the glaucoma stage of the opposite hemiretina worsened, the MTD CNR in the transition from moderate to advanced glaucoma significantly increased. The RNFL CNR in the transition from mild to moderate glaucoma significantly decreased in case of advanced glaucoma in the opposite hemiretina.

Conclusions: Similar to full retinas, detecting conversion to glaucoma in hemiretinas is more sensitive with OCT than SAP, whereas with more advanced disease, SAP is more sensitive for detecting progression. More importantly, the sensitivity for detecting progression in one hemiretina with either technique depends on the glaucoma severity in the opposite hemiretina.

Translational relevance: Monitoring glaucomatous progression with either OCT or SAP partly depends on the glaucoma severity in the opposite hemiretina.

Figure 1.

Example of a 24-2 HFA SITA-Standard visual field test result of a patient's right eye with moderate glaucoma (mean deviation = −10.22 dB).

Figure 2.

Example of an OCT circle scan that was excluded from this study because of a gross segmentation error.

Figure 3.

Hemiretina and corresponding hemifield.

Figure 4.

CNRs of the average RNFL thickness (blue bars) and MTD (green bars) for the superior (dark color bars) and inferior hemiretina (light color bars). The whiskers indicate the upper and lower limit of the 95% confidence interval after bootstrap sampling.

Figure 5.

CNRs of the average RNFL thickness (blue bars) and MTD (green bars) for the transition from mild to moderate (upper panel) and from moderate to advanced glaucoma (lower panel) of the hemiretina under consideration. The x-axis indicates the glaucoma stages of the opposite hemiretina. The whiskers indicate the upper and lower limit of the 95% confidence interval after bootstrap sampling. The asterisk brackets show the statistically significant differences between the CNRs based on the 95% confidence intervals (P < 0.05).

Figure 6.

Example of a segmentation error (yellow arrow) in the superior hemiretina that corresponds to an inferior hemifield with mild glaucoma (MTD-3.1). This segmentation error originates from the segmentation errors in the inferior hemiretina that corresponds to the superior hemifield with advanced glaucoma (MTD-24.3).