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Abstract

The present review will summarize the most updated findings with regards to optical coherence tomography and optical coherence tomography angiography in glaucoma, highlighting their clinical use for detection and monitoring of the disease, and their correlation to functional tests (such as visual field) widely employed in the asset of modern glaucoma clinics.

Keywords

glaucoma optical coherence tomography optical coherence tomography angiography

Introduction

Glaucoma is characterized by progressive retinal ganglion cell (RGC) axonal loss.^1–5

Because RGC axonal thickness is greatest at the peripapillary retina,^6–8 optical coherence tomography (OCT) scans have been mainly used to measure the peripapillary retinal nerve fiber layer (pRNFL) thickness to estimate the glaucomatous structural loss. However, more recent studies demonstrated that glaucoma involves not only RGC axons but also their bodies and dendrites,^9–12 which are mainly located at the macula. Therefore, spectral domain-optical coherence tomography (SD-OCT) scans centered on the macula to measure various inner macular parameters (e.g. ganglion cell complex (GCC), ganglion cell-inner plexiform layer (GCIPL)) are also highly informative of glaucomatous damage, and, combined with optic nerve head (ONH) SD-OCT scans, provide the clinician with a comprehensive understanding of the disease stage.

In the last few years, optical coherence tomography angiography (OCTA) was introduced in the clinical practice, allowing to image the vascular component of this disease, and, possibly, its role in the pathogenesis of glaucoma. Several studies reported decreased OCTA vessel density (VD) both in the peripapillary area and at the macula, corresponding to the location of RGCs’ neural loss in glaucoma.^13–16

The present review will summarize the most updated findings with regards to OCT and OCTA in glaucoma, highlighting their clinical use for detection and monitoring of the disease and their correlation to functional tests (such as visual field) widely employed in the asset of modern glaucoma clinics.

Optical coherence tomography: detection of early glaucoma

It is well established that pRNFL is mainly affected in the inferior and superior quadrants in pre-perimetric and early glaucoma, whereas temporal and nasal quadrants are usually involved later on in the course of the disease.^17–22 Because of this pattern of pRNFL thinning, Wang and colleagues²³ reported that the pRNFL temporal quadrant thickness missed 77% of the eyes showing early GCIPL thinning, suggesting that pRNFL analysis may overlook early glaucomatous macular damage.

At the same time, many studies reported that early superior and inferior pRNFL defects are frequently associated with initial GCIPL changes, which are preferentially located at the inferior-temporal and superior-temporal macular sectors.^12,24–27

Furthermore, it was shown that GCIPL parameters have similar diagnostic accuracy compared to the pRNFL ones in detecting early glaucoma.^28–31

However, it remains unclear whether the glaucomatous damage becomes detectable at the macula or at the peripapillary region first. Kim and colleagues^32,33 explored the relationship between abnormal macular GCIPL thinning and corresponding pRNFL defects on the OCT deviation map. All cases of pRNFL thinning also showed macular GCIPL loss. However, there were several cases of inferior macular GCIPL loss that did not show corresponding pRNFL defects. Therefore, the authors suggested that inferior macular GCIPL changes could be detected statistically before pRNFL changes.^32,33 Triolo and colleagues showed in a cross-sectional study that macular GCIPL and pRNFL normalized thicknesses are affected in similar amount in early glaucoma (Triolo G et al. “Ganglion Cell–Inner Plexiform Layer or Retinal Nerve Fiber Layer: which one is affected first in Early Glaucoma?” Poster presented at: 2016 American Glaucoma Society annual meeting, Fort Lauderdale, Florida, USA). Interestingly, Marshall and colleagues.³⁴ have recently reported that GCIPL precedes pRNFL progression in patients with lower tension glaucoma, whereas patients with higher baseline intraocular pressure (IOP) showed pRNFL progression first.

Peripapillary RNFL and macular GCIPL thicknesses, although extensively utilized, are not the only OCT parameters used for early diagnosis of glaucoma. For instance, the Bruch’s membrane opening derived minimum rim width (BMO-MRW) is another measurement that showed similar accuracy in diagnosing early glaucomatous changes.^35–37 More recently, Zheng and colleagues³⁸ found that among all the parameters taken into account in the study, the supero-temporal and infero-temporal pRNFL and BMO-MRW thicknesses below the fifth percentile yield the best diagnostic performance for glaucoma detection, with Retinal Nerve Fiber Layer Thickness (RNFLT) attaining higher sensitivities than MRW at the same specificity. Surprisingly, BMO-MRW assessment could fail to reveal abnormality even in eyes with confirmed visual field (VF) defects and RNFL abnormalities. Furthermore, integrating RNFLT assessment to BMO-MRW assessment increased the sensitivity of BMO-MRW assessment without compromising the specificities, whereas integrating BMO-MRW assessment to RNFLT assessment did not improve the diagnostic performance. This finding underscores the higher importance of pRNFL thickness analysis in the diagnostic evaluation of glaucoma.

Optical coherence tomography: glaucoma progression

Because of its objective nature and low test–retest variability, structural SD-OCT is a valuable tool to estimate disease progression.³⁹

It is commonly believed that structural OCT is useful only in the pre-perimetric and early stages of the disease, while it is of limited help in advanced glaucoma. This belief is based on the results of several studies showing that pRNFL thickness has greater sensitivity than VF test in eyes with early glaucoma, but not in those with moderate to advanced disease.^40–43 The pRNFL thickness hits the floor earlier than functional measures, usually at mean deviation (MD) values between −8 and −10 dB, preventing recognition of further worsening.^40,41 This is certainly true but applies only to global pRNFL thickness, and recent studies demonstrated that other structural OCT parameters may provide useful information even in patients with advanced disease, where the detection of progression has limitations even with other techniques, due to the minimal residual rim to monitor and the high VF variability in the low sensitivity range.^44,45

Lee and colleagues⁴⁶ recently reported that eyes with advanced glaucoma have preserved regions of pRNFL, which may be used to monitor disease progression. However, it must be kept in mind that quadrants or clock-hours thicknesses are characterized by higher test–retest variability, and, therefore, a greater structural change is required to distinguish true progression from fluctuation.³⁹ Furthermore, glaucoma follows characteristics patterns of OCT progression, whose recognition at the thickness and total deviation maps may help the clinician discriminating between variability and progression.^47,48

Compared to peripapillary OCT parameters, macular OCT measures reach the floor later on and may be useful to monitor patients along the entire spectrum of the disease, including advanced stages.^42,49–51 Belghith and colleagues⁴⁹ were the first to report that mGCIPL thickness may detect progression in advanced glaucoma better than optic nerve measures, such as pRNFL, and further studies corroborated these findings. In the Advanced Imaging for Glaucoma Study, Zhang and colleagues⁴² investigated the utility of pRNFL and macular GCC measures to detect progression in all the spectrum of glaucoma severity and found that pRNFL had little utility in patients with advanced glaucoma as opposed to GCC thickness, which retains high sensitivity throughout all the course of disease. Shin and colleagues⁵⁰ showed that Glaucoma Progression Analysis (GPA) of the GCIPL may be used to track glaucoma progression better than that of pRNFL. In a cohort of patients with advanced glaucoma, Lavinsky and colleagues⁵¹ compared the rates of changes of VF MD, visual field index (VFI), pRNFL, and mGCIPL and found that all the indices but pRNFL rate detected a significant negative rate of change.

There is no consensus on which macular measure (e.g. thickness of GCC, ganglion cell layer (GCL), GCIPL, or full macular thickness (FMT)) best monitors glaucoma progression. A real change must exceed the test–retest variability to be detectable. Previous studies have shown that within-session variability, rather than between-session variability, is responsible for most of the total test–retest variability in macular OCT imaging, and variability is low and uniform (approximately 3 µm) regardless the macular measure considered.^52,53 As the relative variability is related to normal layer thickness, thinner outcome measures (i.e. GCL and GCIPL) have higher relative variability than thicker ones (i.e. GCC and FMT).^52,53 In addition, thinner measures are limited by narrower dynamic ranges and increased variability in severe glaucoma, likely related to segmentation errors which are more frequent with thinner retinal layers.⁵⁴ In yet unpublished studies, GCC was the best outcome measure to monitor glaucoma progression along the entire disease spectrum and provided the best longitudinal structure–function relationship with central VF (Nouri- Mahdavi et al. “Comparison of rates of progression of macular OCT parameters in glaucoma.” Paper presented at: 2018 ARVO annual meeting, Honolulu, HI, USA. Mohammadzadeh et al. “Longitudinal structure-function relationship in the macula.” Paper presented at: 2019 ARVO annual meeting, Vancouver, Canada).

Although MRW is a promising measure to diagnose patients with early glaucoma, few studies suggest that it may perform worse or, at least, equal to pRNFL to monitor disease progression. Bowd and colleagues⁵⁵ estimated the measurement floor of macular GCIPL, pRNFL, and MRW and found that the latter two hit similar measurement floor, which is reached far before macular GCIPL. Gardiner and colleagues⁵⁶ compared pRNFL and MRW in terms of signal-to-noise ratio, which indicates how well a method discriminates a true change from variability; they found that MRW measure has a lower signal-to-noise ratio, and, therefore, it may require a larger magnitude of change than pRNFL to identify significant progression. The conclusions of these theoretical studies were confirmed by Belghith and colleagues,⁴⁹ who showed that both mGCIPL and pRNFL detect more progression than MRW in a cohort of patients with advanced glaucoma. Future studies need to clarify whether MRW can be useful to monitor early glaucoma.

Optical coherence tomography angiography: detection of early glaucoma

OCTA has provided insights into the relationship between neuronal and vascular changes in glaucomatous eyes. Initially, the attention was focused on the peripapillary VD in patients with established glaucoma, and capillary dropouts in these areas corresponding to pRNFL defects were thoroughly demonstrated.^13–16,57 The diagnostic ability of peripapillary VD has been shown to be lower or, at best, equal to pRNFL thickness, and it is still uncertain how this measure may improve the clinical care of glaucoma patients.^16,57,58

More recently, OCTA-based studies have also investigated the macular region and found that glaucomatous eyes have a significantly lower superficial vascular complex (SVC) VD at the macula than healthy eyes. In contrast, no significant difference was found in VD of the intermediate and deep capillary plexuses at the macula.^59–61 The SVC has been found to supply blood to the nerve fiber layer and GCL. Thus, these studies suggested that vascular changes associated with glaucoma occurred preferentially among the vessels that feed the superficial layers of the retina. Similar findings have also been reported in pre-perimetric glaucoma.^61,62

Takusagawa and colleagues,⁵⁹ noticed that a macular area with low perfusion in the SVC corresponded in shape, size, and location to areas of detectable GCC thinning and VF defects. This indicates that there is an intimate correspondence between vascular and structural defect.

Whether macular OCTA parameters are useful in the early diagnosis of glaucoma patients is still under debate. Same studies demonstrated high diagnostic performance of macular OCTA parameters in detecting glaucoma, comparable to those typical of the pVD.^59–61 In a previous study, we compared diagnostic properties of structural OCT and OCTA at the peripapillary and macular regions, and we found that structural parameters (i.e. pRNFL and macular GCIPL) had the best diagnostic performance, followed by radial peripapillary capillaries VD, and, far beyond, macular SVC VD.¹⁶ In this study, high correlation was reported between peripapillary VD and pRNFL, but not between macular VD and mGCIPL in glaucomatous patients. Richter and colleagues⁶³ also reported that peripapillary superficial retinal layer VD has higher diagnostic abilities compared to macular parameters. Wan and colleagues⁶⁴ evaluated the diagnostic performance of macular OCT and OCTA in a large cohort of Chinese patients and, once again, found that macular OCTA performed considerably worse than structural measures. In a study by Park and colleagues,⁶⁵ macular OCTA parameters had an area under the receiver operating characteristic (ROC) curve to discriminate between early glaucoma and healthy controls ranging between 0.50 and 0.60, which is slightly better than tossing a coin. Taken together, these findings may suggest that macular OCTA alone may have a limited role in the diagnosis of early glaucoma patients.

It still needs to be clarified whether the vascular changes at either optic disk or macula are a cause or a consequence of the glaucomatous loss of RGCs and their axons. The fact that the vascular abnormalities detected on OCTA resemble in shape and location the macular GCIPL and pRNFL defects⁵⁹ may suggest that the vascular density is reduced because of the loss of neural tissue, and not the other way around. Furthermore, the fact that the superficial vascular retinal component is selectively affected in glaucoma, and not the deeper plexuses, again suggests that the mechanism is more likely to be somehow related to the neural tissue degeneration rather than being a cause of it. However, longitudinal studies based on OCT and OCTA investigating the very early stage of the disease are needed to elucidate on this topic. These will certainly provide a broader understanding of the pathogenesis of glaucoma and its vascular component.

Optical coherence tomography angiography: glaucoma progression and structure–function correlation

The role of OCT-A in monitoring the disease progression across the glaucoma spectrum is still uncertain. Kim and colleagues⁶⁶ reported that OCT-A is characterized by wider dynamic range than both VF and structural OCT and, therefore, it could be useful to detect progression in severe glaucoma, where VF and structural OCT are limited by high test–retest variability and floor measurement, respectively. Park and colleagues⁶⁷ have shown that baseline reduction in peripapillary choroidal VD was associated with higher odds of VF progression in a cohort of Korean glaucomatous patients. This finding, however, is not surprising as peripapillary atrophy (especially beta) is a well-known risk factor for glaucoma progression and is histologically associated with the loss of choriocapillaris.^68,69 Currently, there is a lack of longitudinal studies investigating the role of OCT-A in the follow-up of glaucoma patients, and there is no evidence that its use in this context may improve the clinical care.

There are few studies investigating the structure–function relationship between VF and OCTA. Ghahari and colleagues⁷⁰ showed that the severity of VF damage is related to both macular and peripapillary VD, along with mGCIPL and pRNFL, and each 1 dB reduction of MD value is associated with a 0.43% and 0.46% reduction in peripapillary and macular OCTA values, respectively. Whether the structure–function relationship is stronger with either OCTA or structural OCT is still uncertain. Yarmohammadi and colleagues^71–73 reported that structure–function correlation between peripapillary OCTA parameters and VF MD was higher than the one between pRNFL and VF MD, and OCTA measures could anticipate VF changes in patients with POAG. Penteado and colleagues⁷⁴ also found a significant association between decreased macular VD and 10–2 VF MD in patients with glaucoma. On the contrary, Wan and colleagues⁶⁴ found that structure–function relationship is much weaker with OCTA than structural OCT.

Conclusion

The advent of clinical OCT has greatly revolutionized the care of glaucoma patients, and this technology has constantly evolved with instruments with better resolution and the introduction of novel parameters.

Peripapillary RNFL has an established role in both the early diagnosis and identification of glaucoma progression, but its measurement floor prevents its usage from monitoring eyes with moderate-to-advance disease. Macular OCT has emerged as a complementary imaging modality to pRNFL. In the glaucoma diagnosis, it fares no worse than pRNFL, and it may be the first sign of OCT damage in some phenotypes of disease, such as normal-tension glaucoma patients. Since macular measures hit the floor later than pRNFL, they may provide useful information also in patients with advanced disease.

Other imaging measures have been welcome with great, perhaps exaggerate, enthusiasm, but their real utility in clinical practice is far from being established. BMO-MRW has a solid rationale and may have some role to diagnose early glaucoma, but it may be of limited utility in the identification of glaucoma progression. OCTA may provide insight into the disease pathogenesis, but it does not offer any clear advantage over structural OCT at the present time.

All these imaging measures provide the clinician with a large amount of information, which may be sometimes discordant. The mental integration of various OCT measures along with the whole clinical picture may allow a better disease evaluation and overcome the limitations of individual parameters.

Footnotes

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iD

Giacinto Triolo

References

Hood

Kardon

. A framework for comparing structural and functional measures of glaucomatous damage. Prog Retin Eye Res 2007; 26: 688–710. doi:10.1016/j.preteyeres.2007.08.001.

Balaratnasingam

CMW

Morgan

Bass

, et al. Axonal transport and cytoskeletal changes in the laminar regions after elevated intraocular pressure. Invest Ophthalmol Vis Sci 2007; 48: 3632–3644. doi:10.1167/iovs.06-1002.

Calkins

Horner

. The cell and molecular biology of glaucoma: axonopathy and the brain. Invest Ophthalmol Vis Sci 2012; 53: 2482–2484. doi:10.1167/iovs.12-9483i.

Crish

Sappington

Inman

, et al. Distal axonopathy with structural persistence in glaucomatous neurodegeneration. Proc Natl Acad Sci U S A 2010; 107: 5196–5201. doi:10.1073/pnas.0913141107.

Buckingham

Inman

Lambert

, et al. Progressive ganglion cell degeneration precedes neuronal loss in a mouse model of glaucoma. J Neurosci 2008; 28: 2735–2744. doi:10.1523/JNEUROSCI.4443-07.2008.

Varma

Skaf

Barron

. Retinal nerve fiber layer thickness in normal human eyes. Ophthalmology 1996; 103: 2114–2119. doi:10.1016/s0161-6420(96)30381-3.

Weinreb

Dreher

Coleman

, et al. Histopathologic validation of Fourier-ellipsometry measurements of retinal nerve fiber layer thickness. Arch Ophthalmol 1990; 108: 557–560. doi:10.1001/archopht.1990.01070060105058.

Budenz

Anderson

Varma

, et al. Determinants of normal retinal nerve fiber layer thickness measured by Stratus OCT. Ophthalmology 2007; 114: 1046–1052. doi:10.1016/j.ophtha.2006.08.046.

Mwanza

Oakley

Budenz

, et al. Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma. Invest Ophthalmol Vis Sci 2011; 52: 8323–8329. doi:10.1167/iovs.11-7962.

10.

Tan

Chopra

, et al. Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology 2009; 116: 2305–2314. doi:10.1016/j.ophtha.2009.05.025.

11.

Greenfield

Bagga

Knighton

. Macular thickness changes in glaucomatous optic neuropathy detected using optical coherence tomography. Arch Ophthalmol 2003; 121: 41–46. doi:10.1001/archopht.121.1.41.

12.

Hood

Raza

de Moraes

, et al. Glaucomatous damage of the macula. Prog Retin Eye Res 2013; 32: 1–21. doi:10.1016/j.preteyeres.2012.08.003.

13.

Jia

Morrison

Tokayer

, et al. Quantitative OCT angiography of optic nerve head blood flow. Biomed Opt Express 2012; 3: 3127–3137. doi:10.1364/BOE.3.003127.

14.

Jia

Wei

Wang

, et al. Optical coherence tomography angiography of optic disc perfusion in glaucoma. Ophthalmology 2014; 121: 1322–1332. doi:10.1016/j.ophtha.2014.01.021.

15.

Liu

Jia

Takusagawa

, et al. Optical coherence tomography angiography of the peripapillary retina in glaucoma. JAMA Ophthalmol 2015; 133: 1045–1052. doi:10.1001/jamaophthalmol.2015.2225.

16.

Triolo

Rabiolo

Shemonski

, et al. Optical coherence tomography angiography macular and peripapillary vessel perfusion density in healthy subjects, glaucoma suspects, and glaucoma patients. Invest Ophthalmol Vis Sci 2017; 58: 5713–5722. doi:10.1167/iovs.17-22865.

17.

Airaksen PJWE. Clinical evaluation of the optic disc and retinal nerve fiber layer. In: Ritch

Shields

(eds) The Glaucomas, vol. 1. 2nd ed. St Louis, MO: Mosby, 1996, pp. 617–658.

18.

Harizman

Oliveira

Chiang

, et al. The ISNT rule and differentiation of normal from glaucomatous eyes. Arch Ophthalmol 2006; 124: 1579–1583. doi:10.1001/archopht.124.11.1579.

19.

Morgan

Bourtsoukli

Rajkumar

, et al. The accuracy of the inferior>superior>nasal>temporal neuroretinal rim area rule for diagnosing glaucomatous optic disc damage. Ophthalmology 2012; 119: 723–730. doi:10.1016/j.ophtha.2011.10.004.

20.

Miki

Medeiros

Weinreb

, et al. Rates of retinal nerve fiber layer thinning in glaucoma suspect eyes. Ophthalmology 2014; 121: 1350–1358. doi:10.1016/j.ophtha.2014.01.017.

21.

Sung

Baek

, et al. Rates and patterns of macular and circumpapillary retinal nerve fiber layer thinning in preperimetric and perimetric glaucomatous eyes. J Glaucoma 2015; 24: 278–285. doi:10.1097/IJG.0000000000000046.

22.

Dave

Shah

. Applicability of ISNT and IST rules to the retinal nerve fibre layer using spectral domain optical coherence tomography in early glaucoma. Br J Ophthalmol 2015; 99: 1713–1717. doi:10.1136/bjophthalmol-2014-306331.

23.

Wang

Raza

de Moraes

, et al. Central glaucomatous damage of the macula can be overlooked by conventional OCT retinal nerve fiber layer thickness analyses. Transl Vis Sci Technol 2015; 4: 4. doi:10.1167/tvst.4.6.4.

24.

Hood

Raza

de Moraes

, et al. The nature of macular damage in glaucoma as revealed by averaging optical coherence tomography data. Transl Vis Sci Technol 2012; 1: 3. doi:10.1167/tvst.1.1.3.

25.

Choi

Jeoung

Park

, et al. Clinical use of an optical coherence tomography linear discriminant function for differentiating glaucoma from normal eyes. J Glaucoma 2016; 25: e162–e169. doi:10.1097/IJG.0000000000000210

26.

Kim

Park

Yoo

, et al. Topographic localization of macular retinal ganglion cell loss associated with localized peripapillary retinal nerve fiber layer defect. Invest Ophthalmol Vis Sci 2014; 55: 3501–3508. doi:10.1167/iovs.14-13925.

27.

Triolo

Vazquez

Monsalve

, et al. Ganglion cell–inner plexiform layer or retinal nerve fiber layer: which one is affected first in early glaucoma? In: American glaucoma society annual meeting (Poster presentation), 03-06 March 2016, Fort Lauderdale, Florida (FL), USA.

28.

Mwanza

Durbin

Budenz

, et al. Glaucoma diagnostic accuracy of ganglion cell-inner plexiform layer thickness: comparison with nerve fiber layer and optic nerve head. Ophthalmology 2012; 119: 1151–1158. doi:10.1016/j.ophtha.2011.12.014.

29.

Mwanza

Budenz

Godfrey

, et al. Diagnostic performance of optical coherence tomography ganglion cell —inner plexiform layer thickness measurements in early glaucoma. Ophthalmology 2014; 121: 849–854. doi:10.1016/j.ophtha.2013.10.044.

30.

Kim

Yang

Lee

, et al. Diagnostic value of ganglion cell-inner plexiform layer thickness in glaucoma with superior or inferior visual hemifield defects. J Glaucoma 2016; 25: 472–476. doi:10.1097/IJG.0000000000000285.

31.

Kim

Yoo

Kim

, et al. Automated detection of hemifield difference across horizontal raphe on ganglion cell—inner plexiform layer thickness map. Ophthalmology 2015; 122: 2252–2260. doi:10.1016/j.ophtha.2015.07.013.

32.

Kim

Jeoung

Park

. Inferior macular damage in glaucoma: its relationship to retinal nerve fiber layer defect in macular vulnerability zone. J Glaucoma 2017; 26: 126–132. doi:10.1097/IJG.0000000000000576.

33.

Kim

, et al. Temporal relation between macular ganglion cell-inner plexiform layer loss and peripapillary retinal nerve fiber layer loss in glaucoma. Ophthalmology 2017; 124: 1056–1064. doi:10.1016/j.ophtha.2017.03.014.

34.

Marshall

Andrew

Hassall

, et al. Macular ganglion cell-inner plexiform layer loss precedes peripapillary retinal nerve fiber layer loss in glaucoma with lower intraocular pressure. Ophthalmology 2019; 126: 1119–1130. doi:10.1016/j.ophtha.2019.03.016.

35.

Chauhan

O’Leary

AlMobarak

, et al. Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography-derived neuroretinal rim parameter. Ophthalmology 2013; 120: 535–543. doi:10.1016/j.ophtha.2012.09.055.

36.

Gmeiner

Schrems

Mardin

, et al. Comparison of Bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment. Invest Ophthalmol Vis Sci 2016; 57: OCT575–OCT584. doi:10.1167/iovs.15-18906.

37.

Toshev

Lamparter

Pfeiffer

, et al. Bruch’s membrane opening-minimum rim width assessment with spectral-domain optical coherence tomography performs better than confocal scanning laser ophthalmoscopy in discriminating early glaucoma patients from control subjects. J Glaucoma 2017; 26: 27–33. doi:10.1097/IJG.0000000000000532.

38.

Zheng

Leung

. Diagnostic criteria for detection of retinal nerve fibre layer thickness and neuroretinal rim width abnormalities in glaucoma. Br J Ophthalmol. Epub ahead of print 30 May 2019. doi:10.1136/bjophthalmol-2018-313581.

39.

Mwanza

Chang

Budenz

, et al. Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus HD-OCT in glaucomatous eyes. Invest Ophthalmol Vis Sci 2010; 51: 5724–5730. doi:10.1167/iovs.10-5222.

40.

Mwanza

Budenz

Warren

, et al. Retinal nerve fibre layer thickness floor and corresponding functional loss in glaucoma. Br J Ophthalmol 2015; 99: 732–737. doi:10.1136/bjophthalmol-2014-305745

41.

Hood

Anderson

Wall

, et al. Structure versus function in glaucoma: an application of a linear model. Invest Ophthalmol Vis Sci 2007; 48: 3662–3668. doi:10.1167/iovs.06-1401.

42.

Zhang

Dastiridou

Francis

, et al. Comparison of glaucoma progression detection by optical coherence tomography and visual field. Am J Ophthalmol 2017; 184: 63–74. doi:10.1016/j.ajo.2017.09.020.

43.

Banegas

Anton

Morilla

, et al. Evaluation of the retinal nerve fiber layer thickness, the mean deviation, and the visual field index in progressive glaucoma. J Glaucoma 2016; 25: e229–e235. doi:10.1097/IJG.0000000000000280.

44.

Rabiolo

Morales

Afifi

, et al. Quantification of visual field variability in glaucoma: implications for visual field prediction and modeling. Transl Vis Sci Technol 2019; 8: 25. doi:10.1167/tvst.8.5.25.

45.

Russell

Crabb

Malik

, et al. The relationship between variability and sensitivity in large-scale longitudinal visual field data. Invest Ophthalmol Vis Sci 2012; 53: 5985–5990. doi:10.1167/iovs.12-10428.

46.

Lee

Joiner

Tsamis

, et al. OCT circle scans can be used to study many eyes with advanced glaucoma. Ophthalmol Glaucoma 2019; 2: 130–135. doi:10.1016/j.ogla.2019.02.004.

47.

Shin

Sung

Park

. Patterns of progressive ganglion cell-inner plexiform layer thinning in glaucoma detected by OCT. Ophthalmology 2018; 125: 1515–1525. doi:10.1016/j.ophtha.2018.03.052

48.

Leung

Weinreb

, et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: patterns of retinal nerve fiber layer progression. Ophthalmology 2012; 119: 1858–1866. doi:10.1016/j.ophtha.2012.03.044.

49.

Belghith

Medeiros

Bowd

, et al. Structural change can be detected in advanced-glaucoma eyes. Invest Ophthalmol Vis Sci 2016; 57: OCT511–OCT518. doi:10.1167/iovs.15-18929.

50.

Shin

Sung

Lee

, et al. Ganglion cell-inner plexiform layer change detected by optical coherence tomography indicates progression in advanced glaucoma. Ophthalmology 2017; 124: 1466–1474. doi:10.1016/j.ophtha.2017.04.023

51.

Lavinsky

Schuman

, et al. Can macula and optic nerve head parameters detect glaucoma progression in eyes with advanced circumpapillary retinal nerve fiber layer damage? Ophthalmology 2018; 125: 1907–1912. doi:10.1016/j.ophtha.2018.05.020.

52.

Miraftabi

Amini

Gornbein

, et al. Local variability of macular thickness measurements with SD-OCT and influencing factors. Transl Vis Sci Technol 2016; 5: 5. doi:10.1167/tvst.5.4.5.

53.

Nouri-Mahdavi

Fatehi

Caprioli

. Longitudinal macular structure-function relationships in glaucoma and their sources of variability. Am J Ophthalmol 2019; 207: 18–36. doi:10.1016/j.ajo.2019.04.034.

54.

Miraftabi

Amini

Morales

, et al. Macular SD-OCT outcome measures: comparison of local structure-function relationships and dynamic range. Invest Ophthalmol Vis Sci 2016; 57: 4815–4823. doi:10.1167/iovs.16-19648.

55.

Bowd

Zangwill

Weinreb

, et al. Estimating optical coherence tomography structural measurement floors to improve detection of progression in advanced glaucoma. Am J Ophthalmol 2017; 175: 37–44. doi:10.1016/j.ajo.2016.11.010.

56.

Gardiner

Boey

Yang

, et al. Structural measurements for monitoring change in glaucoma: comparing retinal nerve fiber layer thickness with minimum rim width and area. Invest Ophthalmol Vis Sci 2015; 56: 6886–6891. doi:10.1167/iovs.15-16701.

57.

Yarmohammadi

Zangwill

Diniz-Filho

, et al. Optical coherence tomography angiography vessel density in healthy, glaucoma suspect, and glaucoma eyes. Invest Ophthalmol Vis Sci 2016; 57: OCT451–OCT459. doi:10.1167/iovs.15–18944.

58.

Rao

Kadambi

Weinreb

, et al. Diagnostic ability of peripapillary vessel density measurements of optical coherence tomography angiography in primary open-angle and angle-closure glaucoma. Br J Ophthalmol 2017; 101: 1066–1070. doi:10.1136/bjophthalmol-2016-309377.

59.

Takusagawa

Liu

, et al. Projection-resolved optical coherence tomography angiography of macular retinal circulation in glaucoma. Ophthalmology 2017; 124: 1589–1599. doi:10.1016/j.ophtha.2017.06.002.

60.

Chen

Liu

, et al. Optical coherence tomography angiography of the superficial microvasculature in the macular and peripapillary areas in glaucomatous and healthy eyes. Invest Ophthalmol Vis Sci 2017; 58: 3637–3645. doi:10.1167/iovs.17-21846.

61.

Kim

Baek

, et al. Topographic correlation between macular superficial microvessel density and ganglion cell-inner plexiform layer thickness in glaucoma-suspect and early normal-tension glaucoma. Br J Ophthalmol 2020; 104: 104–109. doi:10.1136/bjophthalmol-2018-313732.

62.

Hou

Moghimi

Zangwill

, et al. Macula vessel density and thickness in early primary open-angle glaucoma. Am J Ophthalmol 2019; 199: 120–132. doi:10.1016/j.ajo.2018.11.012.

63.

Richter

Chang

Situ

, et al. Diagnostic performance of macular versus peripapillary vessel parameters by optical coherence tomography angiography for glaucoma. Transl Vis Sci Technol 2018; 7: 21. doi:10.1167/tvst.7.6.21.

64.

Wan

Lam

AKN

Leung

. Optical coherence tomography angiography compared with optical coherence tomography macular measurements for detection of glaucoma. JAMA Ophthalmol 2018; 136: 866–874. doi:10.1001/jamaophthalmol.2018.1627.

65.

Park

Kim

Lee

. Macular vessel density and ganglion cell/inner plexiform layer thickness and their combinational index using artificial intelligence. J Glaucoma 2018; 27: 750–760. doi:10.1097/IJG.0000000000001028.

66.

Kim

G-N

Lee

Kim

, et al. Dynamic range of the peripapillary retinal vessel density for detecting glaucomatous visual field damage. Ophthalmology Glaucoma 2019; 2: 103–110. doi:10.1016/j.ogla.2018.11.007

67.

Park

Shin

Jeon

, et al. Association between parapapillary choroidal vessel density measured with optical coherence tomography angiography and future visual field progression in patients with glaucoma. JAMA Ophthalmol 2019; 137: 681–688. doi:10.1001/jamaophthalmol.2019.0422.

68.

Jonas

, et al. Parapapillary atrophy: histological gamma zone and delta zone. PLoS ONE 2012; 7: e47237. doi:10.1371/journal.pone.0047237.

69.

Jonas

Martus

Budde

, et al. Small neuroretinal rim and large parapapillary atrophy as predictive factors for progression of glaucomatous optic neuropathy. Ophthalmology 2002; 109: 1561–1567. doi:10.1016/s0161-6420(02)01098-9.

70.

Ghahari

Bowd

Zangwill

, et al. Association of macular and circumpapillary microvasculature with visual field sensitivity in advanced glaucoma. Am J Ophthalmol 2019; 204: 51–61. doi:10.1016/j.ajo.2019.03.004.

71.

Yarmohammadi

Zangwill

Diniz-Filho

, et al. Peripapillary and macular vessel density in patients with glaucoma and single-hemifield visual field defect. Ophthalmology 2017; 124: 709–719. doi:10.1016/j.ophtha.2017.01.004.

72.

Yarmohammadi

Zangwill

Manalastas

PIC

, et al. Peripapillary and macular vessel density in patients with primary open-angle glaucoma and unilateral visual field loss. Ophthalmology 2018; 125: 578–587. doi:10.1016/j.ophtha.2017.10.029.

73.

Yarmohammadi

Zangwill

Diniz-Filho

, et al. Relationship between optical coherence tomography angiography vessel density and severity of visual field loss in glaucoma. Ophthalmology 2016; 123: 2498–2508. doi:10.1016/j.ophtha.2016.08.041.

74.

Penteado

Zangwill

Daga

, et al. Optical coherence tomography angiography macular vascular density measurements and the central 10–2 visual field in glaucoma. J Glaucoma 2018; 27: 481–489. doi:10.1097/IJG.0000000000000964.

Optical coherence tomography and optical coherence tomography angiography in glaucoma: diagnosis,progression,and correlation with functional tests

Abstract

Keywords

Introduction

Optical coherence tomography: detection of early glaucoma

Optical coherence tomography: glaucoma progression

Optical coherence tomography angiography: detection of early glaucoma

Optical coherence tomography angiography: glaucoma progression and structure–function correlation

Conclusion

Footnotes

Funding

Conflict of interest statement

ORCID iD

References