The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage

Predicting perimetric defects from en face maps of retinal nerve fibre layer reflectance

PURPOSE

To develop criteria to predict visual hemifields with deep perimetric defects based on retinal nerve fibre layer (RNFL) reflectance, in a transparent process whose components can be assessed by independent laboratories analysing data from their own small groups.

METHODS

The analysis was carried out in four stages, using three independent groups of patients-30, 33 and 62 participants-with glaucoma and age-similar controls. The first stage used Group 1 to develop a criterion for RNFL reflectance images at 24, 36 or 48 μm below the inner limiting membrane (ILM). The second stage evaluated the criterion using Group 2. The third stage developed a second criterion to improve performance for Groups 1 and 2 combined. The fourth stage evaluated the second criterion with Group 3. Confidence intervals for sensitivity and specificity were then computed by combining results from all three groups.

RESULTS

The first criterion identified all hemifields with deep defects and no hemifields from controls, using a within-eye reference for healthy RNFL. For Group 2, specificity remained high but sensitivity was reduced. The second criterion improved sensitivity by using location-specific reference values. For Group 3, sensitivity remained high but reduced specificity was found. Confidence intervals showed substantial overlap for the two criteria.

CONCLUSIONS

We developed two criteria to identify patients with deep perimetric defects with high specificity and sensitivity. Several improvements are warranted: automated identification of the fovea-disc angle and optic disc locations, evaluation of normal variation in patterns of RNFL thickness, improved segmentation of ILM and major vasculature, reduction of within-eye variability in RNFL reflectance of healthy eyes, assessment of effects of image quality, assessment of effects of comorbidity and effectiveness of other devices.

Anatomical Features can Affect OCT Measures Used for Clinical Decisions and Clinical Trial Endpoints

Purpose

To understand the association between anatomical parameters of healthy eyes and optical coherence tomography (OCT) circumpapillary retinal nerve fiber layer (cpRNFL) thickness measurements.

Methods

OCT cpRNFL thickness was obtained from 396 healthy eyes in a commercial reference database (RDB). The temporal quadrant (TQ), superior quadrant (SQ), inferior quadrant (IQ), and global (G) cpRNFL thicknesses were analyzed. The commercial OCT devices code these values based on percentiles (red, <1%; yellow, ≥1% and <5%), after taking age and disc area into consideration. Four anatomical parameters were assessed: fovea-to-disc distance, an estimate of axial length, and the locations of the superior and the inferior peaks of the cpRNFL thickness curve. Pearson correlation values were obtained for the parameters and the thickness measures of each of the four cpRNFL regions, and t-tests were performed between the cpRNFL thicknesses coded as abnormal (red or yellow, <5%) versus normal (≥5%).

Results

For each of the four anatomical parameters, the correlation with the thickness of one or more of the TQ, SQ, IQ, and G regions exceeded the correlation with age or disc area. All four parameters were significantly (P < 0.001) associated with the abnormal cpRNFL values. The significant parameters were not the same for the different regions; for example, a parameter could be negatively correlated for the TQ but positively correlated with the SQ or IQ.

Conclusions

In addition to age and disc area, which are used for inferences in normative databases, four anatomical parameters are associated with cpRNFL thickness.

Translational Relevance

Taking these additional anatomical parameters into consideration should aid diagnostic accuracy.

Glaucoma Detection Using Optical Coherence Tomography: Reviewing the Pitfalls of Comparison to Normative Data

PRCIS

Optical coherence tomography is essential in managing glaucoma. This review describes various artifacts that originate from using a normative database to compare the individual's scans. This is a review paper regarding artifacts in optical coherence tomography imaging for glaucoma arising from using a normative database as a reference for healthy retinal nerve fiber layer and ganglion cell layer.

A Model of Progression to Help Identify Macular Damage Due to Glaucoma

The central macula contains a thick donut shaped region of the ganglion cell layer (GCL) that surrounds the fovea. This region, which is about 12 degrees (3.5 mm) in diameter, is essential for everyday functions such as driving, reading, and face recognition. Here, we describe a model of progression of glaucomatous damage to this GCL donut. This model is based upon assumptions supported by the literature, and it predicts the patterns of glaucomatous damage to the GCL donut, as seen with optical coherence tomography (OCT). After describing the assumptions and predictions of this model, we test the model against data from our laboratory, as well as from the literature. Finally, three uses of the model are illustrated. One, it provides an aid to help clinicians focus on the essential central macula and to alert them to look for other, non-glaucomatous causes, when the GCL damage does not fit the pattern predicted by the model. Second, the patterns of progression predicted by the model suggest alternative end points for clinical trials. Finally, the model provides a heuristic for future research concerning the anatomic basis of glaucomatous damage.

Neurofilament Light Chain in Aqueous Humor as a Marker of Neurodegeneration in Glaucoma

Purpose

Neurofilament light chain (NfL) is a neuronal cytoskeletal protein that has been identified as a marker of neurodegeneration in diseases of the central nervous system. In this study, we investigated whether NfL in the aqueous humor (AH) can serve as a marker of neurodegeneration in glaucoma in a racially diverse North American population.

Design

Single-center, case-control study.

Participants

We enrolled patients with various types and stages of glaucoma undergoing planned ophthalmic surgery as part of their routine care and compared them with patients without glaucoma undergoing phacoemulsification for age-related cataract.

Methods

We collected AH from 39 glaucoma patients and 10 patients without glaucoma. AH NfL was quantified using the Single-Molecule Array (Simoa)® NF-light assay (Quanterix). Demographic information, such as age, body mass index, sex, and self-reported race, as well as clinical information, such as pre-operative intraocular pressure (IOP), maximum IOP, and number of pre-operative glaucoma medications, was obtained by reviewing the medical record.

Main Outcome Measures

Levels of AH NfL.

Results

In a model controlling for age and body mass index (BMI), NfL was significantly elevated in AH from glaucoma patients (mean: 429 pg/mL; standard deviation [SD]: 1136 pg/mL) compared to AH from patients without glaucoma (mean: 3.1 pg/mL; SD: 1.9 pg/mg): P = 0.002. Higher AH NfL was associated with higher maximum IOP (R = 0.44, P = 0.005), higher pre-operative IOP (R = 0.46, P = 0.003), and more pre-operative glaucoma medications (Rs = 0.61, P < 0.001). There was no association between AH NfL and Humphrey visual field mean deviation (R = -0.20, P = 0.220), retinal nerve fiber layer thickness as measured with optical coherence tomography (R = 0.07, P = 0.694), or glaucoma stage (Rs = 0.015, P = 0.935).

Conclusion

Our findings suggest that AH NfL may have clinical utility as a marker of glaucomatous neurodegeneration.

Toward a Real-world Optical Coherence Tomography Reference Database: Optometric Practices as a Source of Healthy Eyes

SIGNIFICANCE

The reports from optical coherence tomography (OCT) instruments depend on a reference database (RDB) of healthy eyes. Although these RDBs tend to be relatively small, they are time consuming and expensive to obtain. A larger RDB should improve our ability to screen for diseases such as glaucoma.

PURPOSE

To explore the feasibility of developing a large RDB from OCT scans obtained by optometrists as part of their pre-test gathering of information, we tested the hypothesis that these scans are of sufficient quality for an RDB and contain a relatively low base rate of glaucoma and other pathologies (OPs).

METHODS

Optical coherence tomography widefield (12 × 9 mm) scans from 400 eyes of 400 patients were randomly selected from a data set of more than 49,000 scans obtained from four optometry sites. Based on a commercial OCT report and a previously validated reading center method, two OCT graders categorized eyes as unacceptable to use for RDB, healthy (H), optic neuropathy consistent with glaucoma (ON-G), glaucoma suspect, or OPs.

RESULTS

Overall, 29 (7.25%) of the eyes were graded unacceptable. Of the remaining 371 eyes, 352 (94.9%) were graded H. Although, for one site, 7.4% of the eligible eyes were graded ON-G, the average for the other three sites was 1.4%. Adjustments of the reading center criteria resulted in exclusion of more than half of these ON-G and OP eyes.

CONCLUSIONS

The OCT scans obtained from optometry practices as part of their pre-test regimen are of sufficient quality for an RDB and contain a relatively low base rate of glaucoma and OPs. With the suggested exclusion criteria, the scans from optometry practices that are primarily involved in refraction and medical screening services should yield a large, real-world RDB with improved specificity and a base rate of glaucoma and/or OPs comparable with existing RDB.

Structure-function assessment in glaucoma based on perimetric sensitivity and en face optical coherence tomography images of retinal nerve fiber bundles

Many studies have assessed structure-function relations in glaucoma, but most without topographical comparison across the central 30°. We present a method for assessing structure-function relations with en face images of retinal nerve fiber layer (RNFL) bundles allowing topographical comparison across much of this retinal area. Forty-four patients with glaucoma (median age 61 years) were recruited and tested with Optical Coherence Tomography (OCT) and perimetry. Six rectangular volume scans were gathered, and then montaged to provide en face views of the RNFL bundles. We calculated the proportion of locations showing a perimetric defect that also showed an en face RNFL defect; and the proportion of locations falling on an RNFL defect that also showed a perimetric defect. A perimetric defect for a location was defined as a total deviation (TD) value equal to or deeper than -4 dB. We found that the median (IQR) number of locations with abnormal RNFL bundle reflectance that also had abnormal TD was 78% (60%) and for locations with abnormal TD that also had abnormal RNFL bundle reflectance was 75% (44%). We demonstrated a potential approach for structure-function assessment in glaucoma by presenting a topographic reflectance map, confirming results of previous studies and including larger retinal regions.

Within-eye and between-subject variability for reflectance of the retinal nerve fibre layer

PURPOSE

Reflectance of retinal nerve fibre layer (RNFL) can contribute to detecting the presence of glaucomatous damage and defining its extent. As a step towards developing a normative database for RNFL reflectance, we assessed within-eye and between-subject variability for RNFL reflectance in healthy eyes.

METHODS

Vertical 30° × 15° volume scans at the optic disc were gathered using SD-OCT (Spectralis OCT) from people free of eye disease. Scans were gathered for both eyes of 30 younger adults (mean ± SD = 27 ± 3 years) and for one eye of 30 older adults (68 ± 8 years). Reflectance was quantified for each voxel as the depth-resolved attenuation coefficient (AC). Values for AC were extracted for four slabs (0-52, 24-52, 24-36 and 36-60 μm) and at depths from 24 to 60 μm below the inner limiting membrane (ILM) in 4 μm steps.

RESULTS

Between-subject and within-eye standard deviations (SDs) for the logarithm of AC were similar; median differences were 0.02-0.03 log unit across all four slabs and depths from 24 to 48 μm. Means for the logarithm of AC were higher for younger than older eyes by ~0.1 log unit; this age effect was not due to differences in the raw reflectance of the RNFL, but rather to age-related changes in reflectance of deeper retina affecting the calculation of AC.

CONCLUSIONS

In both groups, within-eye variability in RNFL reflectance near the optic disc was similar to between-subject variability. A better understanding of within-eye variability would be useful for developing normative databases.

Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development

A method for detecting glaucoma based only on optical coherence tomography (OCT) is of potential value for routine clinical decisions, for inclusion criteria for research studies and trials, for large-scale clinical screening, as well as for the development of artificial intelligence (AI) decision models. Recent work suggests that the OCT probability (p-) maps, also known as deviation maps, can play a key role in an OCT-based method. However, artifacts seen on the p-maps of healthy control eyes can resemble patterns of damage due to glaucoma. We document in section 2 that these glaucoma-like artifacts are relatively common and are probably due to normal anatomical variations in healthy eyes. We also introduce a simple anatomical artifact model based upon known anatomical variations to help distinguish these artifacts from actual glaucomatous damage. In section 3, we apply this model to an OCT-based method for detecting glaucoma that starts with an examination of the retinal nerve fiber layer (RNFL) p-map. While this method requires a judgment by the clinician, sections 4 and 5 describe automated methods that do not. In section 4, the simple model helps explain the relatively poor performance of commonly employed summary statistics, including circumpapillary RNFL thickness. In section 5, the model helps account for the success of an AI deep learning model, which in turn validates our focus on the RNFL p-map. Finally, in section 6 we consider the implications of OCT-based methods for the clinic, research, screening, and the development of AI models.