SD-OCT peripapillary nerve fibre layer and ganglion cell complex parameters in glaucoma: principal component analysis

Peripapillary Retinal Nerve Fiber Layer and Macular Ganglion Cell-Inner Plexiform Layer Parameters and Their Associated Factors in Cynomolgus Macaques

Purpose

The purpose of this study was to define the normal range of peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell layer (mGCL), and macular inner plexiform layer (mIPL) thickness in cynomolgus macaques, and explore their inter-relationship and correlation with age, refractive errors, and axial length (AL).

Methods

In this cross-sectional study, we measured biometric and refractive parameters, and pRNFL/mGCL/mIPL thickness in 357 healthy cynomolgus macaques. Monkeys were divided into groups by age and spherical equivalent (SE). Correlation and regression analyses were used to explore the relationship between pRNFL and mGCL/mIPL thickness, and their correlation with the above parameters.

Results

The mean age, SE, and AL were 14.46 ± 6.70 years, -0.96 ± 3.23 diopters (D), and 18.39 ± 1.02 mm, respectively. The mean global pRNFL thickness was 95.06 ± 9.42 µm (range = 54-116 µm), with highest values in the inferior quadrant, followed by the superior, temporal, and nasal quadrants (P < 0.001). Temporal pRNFL thickness correlated positively with age (r = 0.218, P < 0.001) and AL (r = 0.364, P < 0.001), and negatively with SE (r = -0.270, P < 0.001). In other quadrants, pRNFL thickness correlated negatively with age and AL, but positively with SE. In the multivariable linear regression model, adjusted for sex and AL, age (β = -0.350, P < 0.001), and SE (β = 0.206, P < 0.001) showed significant associations with global pRNFL thickness. After adjusting for age, sex, SE, and AL, pRNFL thickness positively correlated with mGCL (β = 0.433, P < 0.001) and mIPL thickness (β = 0.465, P < 0.001).

Conclusions

The pRNFL/mGCL/mIPL thickness distribution and relationship with age, AL, and SE in cynomolgus macaques were highly comparable to those in humans, suggesting that cynomolgus monkeys are valuable animal models in ophthalmic research.

Classifying glaucoma exclusively with OCT: comparison of three clustering algorithms derived from machine learning

BACKGROUND/AIMS

To objectively classify eyes as either healthy or glaucoma based exclusively on data provided by peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell-inner plexiform (GCIPL) measurements derived from spectral-domain optical coherence tomography (SD-OCT) using machine learning algorithms.

METHODS

Three clustering methods (k-means, hierarchical cluster analysis -HCA- and model-based clustering-MBC-) were used separately to classify a training sample of 109 eyes as either healthy or glaucomatous using solely 13 SD-OCT parameters: pRNFL average and sector thicknesses and GCIPL average and minimum values together with the six macular wedge-shaped regions. Then, the best-performing algorithm was applied to an independent test sample of 102 eyes to derive close estimates of its actual performance (external validation).

RESULTS

In the training sample, accuracy was 91.7% for MBC, 81.7% for k-means and 78.9% for HCA (p value = 0.02). The best MBC model was that in which subgroups were allowed to have variable volume and shape and equal orientation. The MBC algorithm in the independent test sample correctly classified 98 out of 102 cases for an overall accuracy of 96.1% (95% CI, 92.3-99.8%), with a sensitivity of 94.3 and 100% specificity. The accuracy for pRNFL was 92.2% (95% CI, 86.9-97.4%) and for GCIPL 98.0% (95% CI, 95.3-100%).

CONCLUSIONS

Clustering algorithms in general (and MBC in particular) seem promising methods to help discriminate between healthy and glaucomatous eyes using exclusively SD-OCT-derived parameters. Understanding the relative merits of one method over others may also provide insights into the nature of the disease.

Study of the ganglion cell complex of the macula by optical coherence tomography in the diagnosis of glaucoma progression

INTRODUCTION

The aim of this work is to evaluate the usefulness of the study of the ganglion cell complex of the macula using the OCT technique to estimate the progression of glaucoma according to its severity.

MATERIAL AND METHODS

This is a retrospective cross-sectional study. It includes 205 eyes of 131 patients with glaucoma or ocular hypertension followed for a mean of 5.7 years. The parameters and rates of three tests have been analyzed using the progression software of each instrument: visual field, optical coherence tomography (OCT) in the ganglion cell complex of the macula and in the nerve fiber layer of the optic nerve. The results of each test, the concordance between them and how they differ according to severity stage have been evaluated.

RESULTS

Visual field classifies more cases of progression in moderate-advanced glaucoma, while in mild glaucoma its capacity is limited. Optic nerve fiber layer OCT classifies more cases of progression in mild glaucoma than in moderate-advanced glaucoma, as it is artifacted by the floor effect. OCT of the macular ganglion cell complex is the test that classifies more cases of progression and has the highest agreement with visual field, regardless of severity.

CONCLUSION

In both mild and moderate-advanced glaucoma, OCT of the macula ganglion cell complex may be a better biomarker of progression than OCT of the macula ganglion cell complex.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Assessment of macular choroidal and retinal thickness: a cohort study in Tibetan healthy children

This research investigates the distribution, progressive changes, and contributing factors of macular choroidal and retinal thickness in Tibetan children utilizing swept-source optical coherence tomography (SS-OCT). The Lhasa childhood study recruited 1632 students from seven primary schools in Lhasa. These participants underwent OCT and ophthalmological evaluations, encompassing retinal and choroidal thickness measurements, refractive error, axial length (AL), and systemic examinations. The median age of the scholars was 8.57 ± 0.50 years with a median spherical equivalent (SE) of 0.19 ± 1.28D. Multivariate regression analysis revealed that thinner macular choroid thickness was correlated with lower value of SE, worse best-corrected visual acuity, higher mean arterial blood pressure (MABP) and boys, while retinal thickness was associated with better image quality and lower value of SE. The choroid and retina were significantly thinner in myopic children. SE was positively related to the thickness of all choroidal and full retinal subregions. In comparison to baseline data from 20 months prior, most regions of the full retina had significantly thinned. Choroidal thickness of Tibetan children is thinner than that of same-age children from other regions. Thinning of retina, the outer-sector GCC and GCIPL may be specified as a follow-up and prognostic indicator for myopia.

Visualising structural and functional characteristics distinguishing between newly diagnosed high-tension and low-tension glaucoma patients

PURPOSE

To determine whether there are quantifiable structural or functional differences that can distinguish between high-tension glaucoma (HTG; intraocular pressure [IOP] > 21 mm Hg) and low-tension glaucoma (LTG; IOP ≤ 21 mm Hg) at diagnosis.

METHOD

This was a retrospective, cross-sectional study. Clinical results of one eye from 90 newly diagnosed HTG and 319 newly diagnosed LTG patients (117 with very-low-tension glaucoma [vLTG; ≤15 mm Hg] and 202 with middling LTG [mLTG; >15 mm Hg, ≤21 mm Hg]) were extracted, which included relevant demographic covariates of glaucoma, quantitative optical coherence tomography (including the optic nerve head, retinal nerve fibre layer and ganglion cell-inner plexiform layer) measurements and standard automated perimetry global metrics. We used binary logistic regression analysis to identify statistically significant clinical parameters distinguishing between phenotypic groups for inclusion in principal component (PC) (factor) analysis (PCA). The separability between each centroid for each cohort was calculated using the Euclidean distance (d(x,y)).

RESULTS

The binary logistic regression comparing HTG and all LTG identified eight statistically significant clinical parameters. Subsequent PCA results included three PCs with an eigenvalue >1. PCs 1 and 2 accounted for 21.2% and 20.2% of the model, respectively, with a d(x,y) = 0.468, indicating low separability between HTG and LTG. The analysis comparing vLTG, mLTG and HTG identified 15 significant clinical parameters, which were subsequently grouped into five PCs. PCs 1 and 2 accounted for 24.1% and 17.8%, respectively. The largest separation was observed between vLTG and HTG (d(x,y) = 0.581), followed by vLTG and mLTG (d(x,y) = 0.435) and lastly mLTG and HTG (d(x,y) = 0.210).

CONCLUSION

Conventional quantitative structural or functional parameters could not distinguish between pressure-defined glaucoma phenotypes at the point of diagnosis and are therefore not contributory to separating cohorts. The overlap in findings highlights the heterogeneity of the primary open-angle glaucoma clinical presentations among pressure-defined groups at the cohort level.

Comparison of Ganglion Cell Layer and Ganglion Cell/Inner Plexiform Layer Measures for Detection of Early Glaucoma

PURPOSE

To test the hypothesis that macular ganglion cell layer (GCL) measurements detect early glaucoma with higher accuracy than ganglion cell/inner plexiform layer (GCIPL) thickness measurements.

DESIGN

Cross-sectional study.

PARTICIPANTS

The first cohort included 58 glaucomatous eyes with visual field mean deviation (MD) ≥ -6 dB and 125 normal eyes. The second cohort included 72 glaucomatous and 73 normal/glaucoma suspect (GS) eyes with scans able to create GCL/GCIPL deviation maps.

METHODS

In the first cohort, 8 × 8 GCL and GCIPL grids were exported and 5 superior and inferior sectors were defined. Global and sectoral GCL and GCIPL measures were used to predict glaucoma. In the second cohort, proportions of scan areas with abnormal (< 5% and < 1% cutoffs) and supernormal (> 95% and > 99% cutoffs) thicknesses on deviation maps were calculated. The extents of GCL and GCIPL abnormal areas were used to predict glaucoma.

MAIN OUTCOME MEASURES

Extents of abnormal GCL/GCIPL regions and areas under receiver operating characteristic curves (AUROC) for prediction of glaucoma were compared between GCL or GCIPL measures.

RESULTS

The average ± standard deviation MDs were -3.7 ± 1.6 dB and -2.7 ± 1.8 dB in glaucomatous eyes in the first and second cohorts, respectively. Global GCIPL thickness measures (central 18° × 18° macular region) performed better than GCL for early detection of glaucoma (AUROC, 0.928 vs. 0.884, respectively; P = 0.004). Superior and inferior sector 3 thickness measures provided the best discrimination with both GCL and GCIPL (inferior GCL AUROC, 0.860 vs. GCIPL AUROC, 0.916 [P = 0.001]; superior GCL AUROC, 0.916 vs. GCIPL AUROC, 0.900 [P = 0.24]). The extents of abnormal GCL regions at a 1% cutoff in the central elliptical area were 17.5 ± 22.2% and 6.4 ± 10.8% in glaucomatous and normal/GS eyes, respectively, versus 17.0 ± 22.2% and 5.7 ± 10.5%, respectively, for GCIPL (P = 0.06 for GCL and 0.002 for GCIPL). The extents of GCL and GCIPL supernormal regions were mostly similar in glaucomatous and normal eyes. The best performance for prediction of glaucoma in the second cohort was detected at a P value of < 1% within the entire scan for both GCL and GCIPL (AUC, 0.681 vs. 0.668, respectively; P = 0.29).

CONCLUSIONS

Macular GCL and GCIPL thicknesses are equivalent for identifying early glaucoma with current OCT technology. This is likely explained by limitations of inner macular layer segmentation and concurrent changes within the inner plexiform layer in early glaucoma.

Optical coherence tomography evaluation of retinal nerve fiber layer thickness in non-arteritic anterior ischemic optic neuropathy and primary open angle glaucoma: a systematic review and Meta-analysis

AIM

To assess the differences in average and sectoral peripapillary retinal nerve fiber layer (pRNFL) thickness using spectral domain optical coherence tomography (SD-OCT) in patients with non-arteritic anterior ischemic neuropathy (NAION) compared with those with primary open angle glaucoma (POAG).

METHODS

A comprehensive literature search of the PubMed, Cochrane Library, and Embase databases were performed prior to October, 2021. Studies that compared the pRNFL thickness in NAION eyes with that in POAG eyes with matched mean deviation of the visual fields were included. The weighted mean difference (WMD) with 95% confidence interval (CI) was used to pool continuous outcomes.

RESULTS

Ten cross-sectional studies (11 datasets) comprising a total of 625 eyes (278 NAION eyes, 347 POAG eyes) were included in the qualitative and quantitative analyses. The pooled results demonstrated that the superior pRNFL was significantly thinner in NAION eyes than in POAG eyes (WMD=-6.40, 95%CI: -12.22 to -0.58, P=0.031), whereas the inferior pRNFL was significant thinner in POAG eyes than in NAION eyes (WMD=11.10, 95%CI: 7.06 to 15.14, P≤0.001). No difference was noted concerning the average, nasal, and temporal pRNFL thickness (average: WMD=1.45, 95%CI: -0.75 to 3.66, P=0.196; nasal: WMD=-2.12, 95%CI: -4.43 to 0.19, P=0.072; temporal: WMD=-1.24, 95%CI: -3.96 to 1.47, P=0.370).

CONCLUSION

SD-OCT based evaluation of inferior and superior pRNFL thickness can be potentially utilized to differentiate NAION from POAG, and help to understand the different pathophysiological mechanisms between these two diseases. Further longitudinal studies and studies using eight-quadrant or clock-hour classification method are required to validate the obtained findings.

External Validation and Clinical Applicability of Two Optical Coherence Tomography-Based Risk Calculators for Detecting Glaucoma

Purpose

To clinically validate the diagnostic ability of two optical coherence tomography (OCT)-based glaucoma diagnostic calculators (GDCs).

Methods

We conducted a retrospective, consecutive sampling of 76 patients with primary open-angle glaucoma, 107 glaucoma suspects, and 67 controls. Demographics, reliable visual field testing, and macular and optic disc OCT were collected. The reference diagnosis was compared against the probability of having glaucoma obtained from two GDCs derived from multivariate logistic regressions using quantitative and qualitative (GDC1) or only quantitative (GDC2) OCT data. The discrimination (area under the curve [AUC]) and calibration (calibration plots) were compared for both calculators and the best OCT parameters.

Results

GDC2 was able to identify 46.9% more suspects and 14.7% more glaucomatous eyes than GDC1. Both GDCs obtained the highest discriminative ability in glaucomatous eyes (GDC1 AUC = 0.949; GDC2 = 0.943 vs inferior peripapillary retinal nerve fiber layer [pRNFL] = 0.931; P = 0.43). The discriminating ability was not as good for glaucoma suspects, but the GDCs were not inferior to pRNFL (GDC 1 AUC = 0.739; GDC2 = 0.730; inferior pRNFL = 0.760; P = 0.54) and GDC2 was still able to correctly identify up to 30.8% more cases than the conventional OCT classification. Calibration showed risk underestimation for both groups and calculators, but it was better in GDC2 and in patients with glaucoma.

Conclusions

OCT-based calculators showed an excellent diagnostic performance in glaucomatous eyes. GDC2 was able to identify approximately 30% more cases than the conventional pRNFL inferior OCT classification in both groups, suggesting a potential role of these composite scores in clinical practice.

Translational Relevance

These OCT-based calculators may improve glaucoma diagnosis in clinical care.

Retinal ganglion cell layer thickness and volume measured by OCT changes with age, sex, and axial length in a healthy population

Background

The ganglion cell layer (GCL) measurements with Optical Coherence Tomography (OCT) are important for both ophthalmologists and neurologists because of their association with many ophthalmic and neurological diseases. Different factors can affect these measurements, such as brain pathologies, ocular axial length (AL) as well as age and sex. Studies conducted to measure the GCL have overlooked many of these factors. The purpose of this study is to examine the effect of age, sex, and AL on normal retinal GCL thickness and volume in a healthy population without any neurological diseases.

Methods

A prospective cross-sectional study was designed to measure GCL thickness and total volume with OCT with automated segmentation and manual correction where needed. Visual acuity, AL, and autorefraction were also measured. A mixed linear model was used to determine the association of the effect of the various parameters on the GCL thickness and volume.

Results

One hundred and sixteen eyes of 60 subjects (12–76 years of age, 55% female) were examined of which 77% had 0 ± 2 D of spherical equivalent, and mean axial length was 23.86 mm. About 25% of the OCT-automated GCL measurements required manual correction. GCL thickness did not differ in similar anatomic regions in right and left eyes (P > 0.05). GCL volume was greater in males relative to females after adjustment for age and axial length (1.13 ± 0.07 mm³ for males vs 1.09 ± 0.09 mm³ for females; P = 0.031). GCL thickness differed between males and females in the inner retinal ring (P = 0.025) but not in the outer ring (P = 0.66). GCL volume declined with age (P = 0.031) but not after adjustment for sex and axial length (P = 0.138). GCL volume declined with longer axial length after adjustment for age and sex (P = 0.048).

Conclusion

Age, sex and axial length should be taken into consideration when measuring the GCL thickness and volume with OCT. Automated OCT segmentation should be reviewed for manual adjustments.