- 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- 2Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- 3Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- 4Department of Ophthalmology, Stanford University, Palo Alto, CA, United States
- 5Department of Neurology and Neurosciences, Stanford University, Palo Alto, CA, United States
Introduction: While magnetic resonance imaging is currently the primary diagnostic tool for pituitary tumors, optical coherence tomography (OCT) may be used in evaluating the visual pathway impact of these lesions. This study evaluates the utility of OCT in patients with chiasmal compression from para-chiasmal lesions and determines its role in predicting visual field outcomes post-operatively.
Methods: A search of five databases identified OCT studies in patients with neoplasms affecting the optic chiasm. Meta-analyses compared i) healthy controls versus patients, ii) good versus poor visual recovery post-operatively, and iii) patients with visual field defects (VFDs) versus those without. Standardized mean differences (SMDs) and mean differences (MDs) were used.
Results: A review of 97 studies (5,300 patient eyes and 2,209 controls) demonstrated significantly thinner peripapillary retinal nerve fiber layer (pRNFL), macular RNFL (mRNFL), macular ganglion cell complex (mGCC), and macular ganglion cell–inner plexiform layer (mGCIPL) in patients as compared to controls. On pRNFL analysis, four-sector analysis demonstrated that patients had thinner RNFL in all quadrants compared to controls, with the greatest thinning in the inferior quadrant (MD −16.37 μm [−22.35, −10.39]) and the least in the nasal quadrant (MD −10.91 μm [−16.45, −5.38]). mRNFL analysis showed the greatest thinning in the supero-nasal (MD −11.57 μm [−19.32, −3.83]) and infero-nasal sectors (MD −11.39 μm [−17.38, −5.40]). The meta-analysis of mGCIPL sectors found the infero-nasal region to have the most thinning. Patients with good visual recovery had higher pre-operative mean pRNFL thickness (MD 11.35 μm [6.20, 16.49]).
Discussion: Associations between OCT changes, neoplasms affecting the optic chiasm, and visual outcomes demonstrate its potential to support diagnosis and prognosis for patients with para-chiasmal lesions. Further research is needed to ascertain the relevance of pre-perimetric OCT changes.
1 Introduction
The optic chiasm is located superior to the pituitary gland and inferior to the hypothalamus (1). Due to their anatomical proximity, lesions of structures adjacent to the optic chiasm can result in visual field (VF) defects, the classical bitemporal hemianopia (2). In clinical practice, such VF defects can be assessed quantitatively using perimetry (3), while objective damage to the retinal ganglion cells can be assessed using non-invasive retinal imaging such as optical coherence tomography (OCT) (4). OCT utilizes infrared light to generate cross-sectional images of the eye at resolutions of 5–20 μm (5) and has been reported to be more sensitive for the detection of chiasmal impact by para-chiasmal lesions than visual field testing (6). Visual field testing and OCT complement magnetic resonance imaging (MRI) detect and characterize small soft tissue changes in the region of the chiasm (7, 8) by demonstrating the functional damage and microstructural visual pathway damage caused by para-chiasmal lesions, respectively. This study aimed to evaluate the utility of OCT in evaluating patients with chiasmal compression from para-chiasmal lesions compared to controls and to determine its role in predicting visual field outcomes post-operatively and monitoring patients pre-operatively.
2 Methods
2.1 Search strategy and information sources
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were utilized (9).
A search of PubMed, Embase, SCOPUS, CINAHL, and Web of Science was conducted from the inception of the databases until August 2024. An additional 24 papers from previous studies were also included in the search. The search terms and strategies can be found in the Supplementary Material. In addition, the reference lists of identified studies were reviewed, and any additional studies meeting the inclusion criteria were also included in the review.
2.2 Selection process and eligibility criteria
Two independent reviewers (KSAL and WXAT) assessed the studies for inclusion. The inclusion criteria were as follows: i) studies that utilized OCT; ii) studies with subjects with para-chiasmal neoplasms; iii) studies comparing subjects with and without para-chiasmal lesions (such as pituitary tumors, meningiomas, craniopharyngiomas, or Rathke’s cleft cysts) or subjects with good versus poor post-operative VF outcomes; and iv) studies that were published since 2010. The exclusion criteria were as follows: i) studies that were reviews, systematic reviews, meta-analyses, case reports, guidelines, letters, or protocols; ii) studies that were not in English; iii) studies that were not conducted in humans; and iv) studies where the number of eyes studied was less than 10. The number of eyes was selected based on studies suggesting that the minimum number of participants in a study should be nine (10) and to reduce the number of underpowered studies, which may introduce bias and heterogeneity in a meta-analysis (11).
2.3 Data extraction and analysis
Retrieved data were uploaded into EndNote X20 and imported into the COVIDENCE Systematic Review Software (Veritas Health Innovation, Melbourne, Australia) for screening. Inconsistencies during screening were resolved by discussion or by a third reviewer’s intervention.
Data extracted from the papers included
1. authors, year of publication, and sample size; and
2. patients’ characteristics and disease status.
OCT measurements included the peripapillary retinal nerve fiber layer (pRNFL), macular retinal nerve fiber layer (mRNFL), macular ganglion cell complex, and macular ganglion cell–inner plexiform layer (mGCIPL).
For each of two comparisons (patients vs. control, and good vs. poor visual field outcomes), meta-analyses were performed for OCT measurements reported in the form of mean and standard deviation in four or more studies. For other comparisons, OCT measurements reported in other formats (such as median or mean with an interquartile range) or OCT measurements reported in fewer than four studies, meta-analyses were not performed. If two studies report on the same study group but have differing outcomes, both studies may be included. However, if similar outcomes are reported, the studies may be excluded from analysis (12). If a study reported on the results of both eyes of a study subject, the better eye would be chosen to reduce the selection bias of significant results.
Meta-analyses for outcomes were conducted in RevMan, Version 5.4 (Nordic Cochrane Centre) to evaluate standardized mean differences (SMDs) and mean differences (MDs) for the parameters that were reported in mean and standard deviation. SMD allows for the comparison of parameters regardless of the OCT models or patient demographics, such as age and gender (13), while MD allows for the pooling of the average differences in OCT parameter thicknesses between the subjects studied (14). Results for SMD are reported in standard deviations (SDs), while MDs are reported in micrometers (μm).
Heterogeneity was assessed using I2, a statistic that describes the percentage of the variability in effect estimates due to heterogeneity rather than sampling errors, with low, moderate, and high levels set at 25%, 50%, and 75%, respectively (15). In cases of moderate or high levels of heterogeneity, a random-effects meta-analysis model was used; otherwise, a fixed-effects model was utilized.
Quality and risk of bias assessments were conducted using the QUADAS-2 tool, which assesses patient selection, index test, reference standard, flow, and timing of the diagnostic tests, along with applicability (16). Authors KSAL and WXAT independently assessed study bias. Disagreements were resolved with discussion or with a third-party review. The significance level of all tests was set at p < 0.05.
3 Results
3.1 Study selection
A total of 710 studies were identified, 106 were sought for full-text review, and 97 were included for this review (Figure 1). Risk of bias information can be found in the Supplementary Material.
Figure 1. PRISMA study selection flowchart. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
3.2 Study characteristics
A total of 97 studies with a total of 5,300 eyes and 2,209 control eyes were reviewed (Table 1) (2, 17–112). Of these studies, 51 were not included in prior meta-analyses, and 43 studies had results that were utilized for the meta-analysis comparing patients and controls (Figure 2).
OCT devices that were used included Zeiss Cirrus OCT (26 studies), Zeiss Stratus OCT (nine studies), Topcon DRI OCT (seven studies), Topcon OCT (seven studies), Nidek RS-3000 (five studies), Optovue OCT (five studies), Optopol Revo (one study), Optovue RTVue (17 studies), Heidelberg Spectralis OCT (24 studies), and OTI Spectral OCT (one study) (Table 2). Among the 97 studies, 83 studies utilized spectral-domain OCT (SD-OCT), nine utilized time-domain OCT (TD-OCT), and seven utilized swept-source OCT. Seven studies utilized two devices in their analysis of patients (28, 44, 52, 70, 72, 84, 86). Due to the small number of studies utilizing time-domain and swept-source OCT, subgroup analysis was not performed. Two articles were unclear regarding the device studied (22, 69), while another article likely had a typo in the device name, and a search of the articles within the same department revealed that it utilized the Nidek RS-3000 (84).
In the 97 studies, a majority utilized the Humphrey Visual Field Perimeter; others utilized the Octopus Perimeter, Goldmann Perimeter, Kowa Perimeter, Centerfield Perimeter, MS Westfalia Perimeter, and the Vision Monitor Perimeter. Mean deviation was the most reported perimeter index and was used in 67 studies (Table 3). A total of 14 studies were utilized to compare good and poor visual field outcomes post-operatively (Figure 3). The studies that were not utilized for meta-analysis did not present the data in mean and standard deviation or had comparisons that did not have four or more studies.
3.3 OCT parameters in patients vs. controls
3.3.1 Peripapillary retinal nerve fiber layer analysis
Retinal nerve fiber layer (RNFL) thickness scans were obtained at the optic nerve head in a circular linear scan for pRNFL analysis. Depending on the device used, the average thickness was split into four or six sectors. The four-sector scan was divided into superior, temporal, inferior, and nasal; the six-sector scan was divided into nasal, supero-nasal, supero-temporal, temporal, infero-temporal, and infero-nasal. A total of 38 papers compared the pRNFL thicknesses in patients versus controls (Supplementary Figure 1, 29). The mean pRNFL thickness was thinner in patients when compared to controls, with an SMD of −1.02 SD [−1.27, −0.78] (Table 4) and an MD of 12.23 μm [−15.43, −9.03].
In the four-sector pRNFL analysis, 20 studies were analyzed for the superior sectors, 18 studies for the inferior sectors, 19 studies for the nasal sectors, and 20 studies for the temporal sectors (Supplementary Figures 2–5, 30–33). In all of these studies, patients demonstrated thinner RNFL in every sector as compared to controls (Table 4). The inferior quadrant had the greatest thinning compared to the other sectors, with an MD of −16.37 μm [−22.35, −10.39]. The quadrant with the least thinning was the nasal quadrant with an MD of −10.91 μm [−16.45, −5.38].
In the six-sector pRNFL analysis, four studies were analyzed (Supplementary Figures 6–11, 34–39). When comparing between patients and healthy controls, the pRNFL in the nasal, temporal, supero-temporal, and infero-temporal sectors was significantly thinner in patients, as evidenced by the SMD and MD. In the nasal sector, the SMD was −0.76 SD [−1.42, −0.09], and the MD was −12.70 μm [−21.97, −0.43]. In the temporal sector, the SMD was −1.08 SD [−1.78, −0.39], and the MD was −14.03 μm [−24.35, −3.70]. The supero-temporal sector had an SMD of −1.03 SD [−1.23, −0.82] and an MD of −21.95 μm [−26.03, −17.87]. Lastly, the infero-temporal region had an SMD of −0.98 SD [−1.32, −0.63] and an MD of −22.90 μm [−31.96, −13.84] (Table 4). This difference in thinning was not observed in the supero-nasal and infero-nasal sectors (Table 4). In the supero-nasal sector, the SMD was −0.60 SD [−1.30, 0.10] and the MD was −13.53 μm [−29.92, 2.85]; in the infero-nasal sector, the SMD was −0.57 SD [−1.44, 0.29], and the MD was −11.46 μm [−34.77, 11.86].
3.3.2 Macular retinal nerve fiber layer analysis
For mRNFL analysis, the scans were centered on the fovea. The mean thickness over the scanned area was analyzed in the form of a macular grid, after which data in the form of an Early Treatment of Diabetic Retinopathy Study (ETDRS) circle or as a box can be extracted. A total of 11 papers compared the macular RNFL between patients and controls (19, 30, 48, 49, 60, 64, 71, 93, 94, 110, 111). When analyzed using SMD and MD, the mean macular RNFL thicknesses were thinner in patients than in controls (Supplementary Figure 12, 41, Table 4).
In box analysis, all four sectors (supero-nasal, supero-temporal, infero-nasal, and infero-temporal) were thinner in patients (Supplementary Figures 13–16, 41–44). Four studies were included in this comparison. As compared to the other sectors, there was greater thinning of the mRNFL layer in patients in the supero-nasal sector with an MD of −11.57 μm [−19.32, −3.83] and the infero-nasal sector with an MD of −11.39 μm [−17.38, −5.40] as compared to healthy controls (Table 4).
There were insufficient papers for the analysis of subsectors presented in the ETDRS circle.
3.3.3 Macular ganglion cell complex analysis
The macular ganglion cell complex (mGCC), which includes the three innermost retinal layers (i.e., the nerve fiber layer, the ganglion cell layer, and the inner plexiform layer) at the macula, was studied in 21 papers comparing patients to controls (2, 19, 23, 26, 28, 34, 36, 37, 46, 50, 51, 59, 60, 72, 94–96, 99, 104, 106, 112). These layers were analyzed by both the total mean values and the superior and inferior hemispheres of the mGCC (Supplementary Figures 17–19, 45–47). It was found that the mean thickness and hemispheric mGCC thickness were significantly thinner in patients as compared to controls on both MD and SMD analyses, with the superior mGCC being −6.08 μm [−9.67, −2.49] thinner and inferior mGCC being −5.73 μm [−8.83, −2.63] thinner (Table 4).
Four studies evaluated nasal and temporal hemispheric GCC; however, two studies reported on the same patient group with similar OCT results (60, 95); hence, this analysis could not be performed (12).
3.3.4 Macular ganglion cell–inner plexiform layer analysis
There were 11 papers that studied the mGCIPL thickness measurement differences between patients and controls (17, 19, 40, 52, 54, 64, 73, 85, 93, 94, 109). Analysis was split into mean analysis and six circumferential sectoral analyses (Supplementary 20–23, 48–52). The mean mGCIPL was found to be thinner in patients as compared to healthy controls with an SMD of −1.63 SD [−2.55, −0.71] and an MD of −8.25 μm [−12.16, −4.35] (Table 4).
For sectoral analysis, meta-analysis was conducted for the superior, supero-nasal, and infero-nasal sectors, as these sectors met the analysis criteria requiring four or more studies with analyzable data. Meta-analysis revealed that patients had thinner superior, supero-nasal, and infero-nasal mGCIPL layers as compared to healthy controls (Table 4). The infero-nasal mGCIPL showed the greatest thinning with an MD of −14.15 μm [−23.10, −5.19], while the superior sector showed the least thinning with an MD of 12.98 μm [−19.18, −4.35].
3.3.5 Macular ganglion cell layer analysis
There were 13 studies that evaluated ganglion cell layer thickness measurements, all of which were at the macula (20, 21, 30, 48, 49, 55, 75–77, 90, 108, 110, 111). However, for the comparisons studied by the 13 studies, none of the comparisons met our criteria requiring four or more papers presenting data amenable to meta-analysis. It was reported in seven studies that ganglion cell layer thicknesses were thinner in patients as compared to controls (20, 30, 48, 49, 75, 110, 111).
3.4 OCT parameters in good vs. poor VF outcomes
A total of 14 studies analyzed the differences in OCT measurements pre-operatively in patients who had good visual function recovery following operation versus those with poor or no recovery (33, 41, 44, 47, 49, 50, 57, 69, 78, 97, 98, 105, 107, 108). In eight of these 14 studies, the data provided by the studies allowed for meta-analysis, as they were presented in mean and standard deviation formats. pRNFL was analyzed using the mean pRNFL, as well as by the superior, temporal, nasal, and inferior sectors (Supplementary Figures 24–28, 52–56). Pre-operative mean pRNFL thicknesses were lower in patients who had poor VF recovery as compared to those with good recovery (Table 4). Patients with good visual recovery had a thicker RNFL than patients without good visual recovery, with an MD of 11.35 μm [6.20, 16.49]. On sectoral analysis, pRNFL measurements in the superior, inferior, and temporal quadrants were thicker in patients with good visual recovery as compared to patients with poor or no recovery, while the nasal pRNFL demonstrated a lack of difference between the two groups. For the superior quadrants, the SMD was 0.42 SD [0.24, 0.60] and the MD was 9.42 μm [3.49, 15.35]. In the inferior quadrants, the greatest difference was seen, with an SMD of 0.62 SD [0.25, 0.99] and an MD of 10.17 μm [4.35, 15.98]. The temporal quadrant had an SMD of 0.62 SD [0.18, 1.05] and an MD of 8.35 μm [3.28, 13.42].
3.5 Visual field defects versus no visual field defects
No studies met the criteria for the analysis comparing patients presenting with visual field defects against patients without visual field defects.
4 Discussion
We conducted a systematic review and meta-analysis of the existing literature to identify studies where OCT was utilized in para-chiasmal lesions, and we evaluated the utility of OCT in the diagnosis, prognostication, and monitoring of these patients. We also included the analysis of mRNFL sectorally, conducted a meta-analysis for pre-operative pRNFL in patients with good visual recovery, added more papers for the meta-analysis than other studies, and analyzed different device models and brands.
We found that OCT has a role in demonstrating the microstructural damage caused by the compression on the optic chiasm as seen by the reduction in thicknesses of the pRNFL, mRNFL, mGCC, and mGCIPL in patients as compared to controls. Furthermore, we observed that patients with better visual recovery had thicker pre-operative pRNFL, which may guide prognostication. Our findings further support existing literature (113) that there may be a role for OCT in the evaluation of patients with para-chiasmal lesions.
4.1 Update of meta-analysis to the existing literature
In our study, the results largely support a prior meta-analysis by Jeong in 2022 (113) and Chou in 2020 (114), who identified significant thinning in OCT parameters in patients with para-chiasmal lesions. We also sought to clarify the results obtained by the previous meta-analysis to ensure that the results are coherent. In updating the meta-analysis, we included 49 more papers for the mean pRNFL analysis. We split the analysis of pRNFL and mRNFL in contrast to Jeong, but we found that there was no significant difference between the use of either measurement. To our knowledge, our study is the first to meta-analyze the mRNFL sectorally, demonstrating sectoral thinning corresponding to that of the visual fields, potentially providing a better anatomical–functional measure corresponding to the damage caused by para-chiasmal lesions. We also updated the findings for mGCC and mGCIPL with 11 and three more papers added, respectively, as compared to Jeong’s paper, further substantiating the results of the analysis of mGCIPL by Jeong. We also further conducted analysis on sectoral measurements of the various OCT parameters to further identify pathological patterns seen in patients with para-chiasmal lesions. While there may be a role for OCT in the monitoring of patients prior to the development of visual field defects, this requires more evidence.
4.2 OCT’s role in the evaluation of patients
The role of OCT is to allow for a structural analysis of the retinal microstructure, which is not amenable to visualization through MRI or perimetry (115). Retinal thickness measurements may reflect axonal loss even possibly before visual field defects are present (116), potentially allowing for pre-perimetric monitoring of patients with radiologically diagnosed para-chiasmal lesions. The use of OCT has been suggested to be used in conjunction with an MRI in other conditions, such as multiple sclerosis, possibly as an alternative for monitoring the disease (117).
The advancement in the technology of OCT, in the form of SD-OCT and more recently swept-source OCT, allows for better segmentation of the nerve fiber layers for improved analysis as compared to TD-OCT (118). Our study found that a majority of authors utilized SD-OCT and that only a few studies utilized TD-OCT. Through the utilization of SMD, where the mean differences are transformed to a common scale, the differences between the OCT machines were accounted for in the analysis (14, 119), allowing for the generalization of the results (120). Furthermore, in a previous study by Colin et al., with manual adjustment in SD-OCT segmentation lines, the measurements are comparable to those of TD-OCT, allowing for comparison between trials utilizing different OCT machines (121).
4.2.1 Patients versus controls
For patients with para-chiasmal lesions, our meta-analysis confirms that OCT parameters demonstrate significant thinning in patients when compared to controls. Through the use of the standardized mean difference, it is demonstrated that, regardless of the machine model used, patients have reduced OCT parameters compared to controls. On further analysis with mean differences, depending on the sector analyzed, an average of >10-μm thinning in pRNFL parameters, >5-μm thinning in mGCC, >8-μm thinning in GCIPL, and >1.87-μm thinning in mRNFL parameters were seen.
Sectorally, the nasal, naso-superior, and naso-inferior peripapillary fibers were demonstrated to have smaller magnitudes of thinning as compared to the temporal, temporo-superior, and temporo-inferior fibers in patients with para-chiasmal lesions as seen on SMD analysis. This corresponds to the classical bitemporal hemianopia caused by pituitary adenomas in view of the Garway–Heath map of the structural–functional relationship between the visual fields and the peripapillary nerve fiber layer (122–124).
This lower magnitude of thinning of the nasal pRNFL was also previously noted by the meta-analysis of Chou et al. (114). Previous understanding of how the nerve fiber layers enter the optic nerve head has been a topic of debate, with nerve fibers nasal to the optic disc entering the disc nasally, while those temporal to the optic disc but nasal to the macula do not have clear origins (125). Our findings are consistent with the Garway–Heath map. In the Garway–Heath map, the nasal pRNFL fibers correlated to a smaller portion of the nasal hemifield. Due to the large number of foveal fibers entering the optic nerve head temporally (126), this would likely account for the greater thinning in the temporal pRNFL as compared to the nasal pRNFL. Patients with bitemporal hemianopia would therefore have more thinning in the temporal optic nerve head fibers due to the nasal hemiretinal fibers entering the optic disc temporally.
Our updated meta-analysis contradicts the more recent analysis by Jeong et al., who noted that the nasal RNFL has greater magnitudes of thinning (113). In Jeong’s study, the analysis of the RNFL was conducted with both peripapillary RNFL and macular RNFL, which may have confounded the results. As the nasal pRNFL and nasal mRNFL do not correspond to the distribution of the nerve fibers (123), our outcomes differed from Jeong’s.
Furthermore, the analysis of the macular RNFL showed that when scanning the macula with a box-shaped configuration, the nasal sectors demonstrated greater thinning as compared to the temporal sectors. This corresponds to the crossing over of the nasal hemiretinal fibers at the optic chiasm (94). It is highlighted that the classical bitemporal hemianopia distribution of visual field defects seen on perimetry is in connection with the fovea; thus, this is congruent with our findings that nasal sectors at the macular RNFL are thinner than the temporal sectors. This may be easier to interpret than the peripapillary RNFL. However, since there were only four studies evaluating RNFL at the macula, more studies would be needed to support the role of nasal hemiretinal RNFL evaluation in patients with optic chiasm lesions, as well as retinotopic maps of the nerve fiber decussations.
4.3 OCT’s role in prognosis for VF recovery
Current prognostic factors, such as the patient’s age, pre-operative visual field deficits, visual acuity, and presence of optic disc atrophy, do not fully predict post-operative visual field recovery (33). As demonstrated in our study, OCT may be used to identify the potential for visual recovery following surgery. OCT parameters allow for the quantification of permanent axonal loss, which includes an additional measurement of damage made because of para-chiasmal lesions (78). As our study demonstrated, pre-operative pRNFL was thicker in patients with good visual field recovery as compared to those with poor visual recovery. Sectoral analysis suggests that thinning of the superior, inferior, and temporal sectors is seen in patients with poorer visual outcomes. This suggests that the permanent axonal loss may be less in patients with good visual field recovery, and sectoral analysis may be utilized to further prognosticate patients.
4.3.1 Relationship between pre-operative visual deficits and VF recovery
In a prior study, Jeon et al. (41) found that the retinal thickness, including pRNFL and mGCIPL, did not show a relationship with post-surgical visual field defect (VFD) improvement. Jeon argued that in other studies, including a paper by Moon et al. (110), the pre-operative visual field and visual acuity were already significantly different between the two VF populations and thus were not representative of the OCT’s prognostic ability. However, this study demonstrates that most papers did not have patients with significant pre-operative differences in functional visual deficits between the visual recovery and non-recovery groups. In five out of eight included studies (41, 47, 78, 98, 105) that compared pre-operative visual acuity and mean differences in visual field, no significant pre-operative differences in these variables were observed. Two out of eight studies showed significant differences in pre-operative visual function (visual field and/or visual acuity) in patients with post-operative visual recovery and those without visual recovery (97, 108). Lastly, Garcia et al. did not compare pre-operative visual acuity and mean differences in visual fields (33).
In our study, we found that pre-operative mean pRNFL thickness was lower in patients with poor VF recovery as compared to those with good recovery. All sectors, other than the nasal sectors, were significantly thinner in patient groups that did not have visual recovery, win an MD ranging from 8.35 μm [3.28, 13.42] to 11.35 μm [6.20, 16.49] (Table 4). This suggests that pre-operative RNFL thickness may have a prognostic value.
4.3.2 Identification of a cut-off for predicting visual field recovery
In our systematic review, several studies have attempted to identify cut-offs for predicting visual field recovery utilizing various parameters. For pRNFL and mRNFL, the authors identified various cut-offs (44, 45, 49, 57, 127). Lee’s study demonstrated the highest sensitivities of >80% for visual field recovery with cut-offs of 24.5, 17, 26, and 25.5 μm for the superior, temporal, nasal, and inferior sectors of mRNFL, respectively. Kawaguchi noted that the pRNFL thicknesses of the temporal quadrants being <49 μm had an odds ratio of 15.6 for poor visual outcome. However, there was no unified cut-off for pRNFL or mRNFL thicknesses that predicted visual recovery best. macular Ganglion Cell Layer (mGCL) was also analyzed by Lee, Yoo, and Moon (108, 110). The last author found sensitivities and specificities for visual field recovery of >100% with a cut-off of 30.6 μm for mGCL thickness. For mGCC, only Mambour et al. (57) looked at this parameter with an area under the curve of >0.9 for mGCC thickness of ≥67 μm and mRNFL thickness of ≥75 μm.
The wide range of cut-offs of the different parameters cited by the above authors presents challenges to the clinical application of a cut-off for the prognostication of likely poor post-operative outcomes. However, from the above studies, macular parameters appear to have the best potential for being good predictors of visual field recovery, as seen by the sensitivities and specificities being >80% reported by the studies (49, 108).
4.4 OCT’s role in monitoring disease progression
In the management of pituitary adenomas, visual impairments and related symptoms secondary to mass effect are an indication for surgery with goals to prevent the progression of symptoms and to reverse symptoms (128). Our systematic review identified the potential for OCTs to be utilized in monitoring patients as VFDs progress. In Orman’s study (111), it was found that prior to the development of VFDs on perimetry, macular OCT parameters were already shown to be thinner. This raises the possibility of using OCT as a pre-perimetric clinical monitoring tool to detect optic nerve damage when VF testing is unavailable, unreliable, or prior to true VF defects. Wang et al. (101) found that when comparing patients with sellar mass but without VFD against healthy controls, although the pRNFL of patients was thinner, it was not statistically significant. This was, however, suggested to be due to smaller sample sizes or due to varying degrees of disease progression.
4.5 Clinical utilization of OCT
Currently, in the monitoring and diagnosis of para-chiasmal lesions, MRI with contrast remains the gold standard tool (7). OCT has the benefits of convenience and safety as compared to the MRI, given the lack of contrast, ease of conduct, and price differentials. As this study has shown, patients with para-chiasmal lesions have lower retinal layer thicknesses as compared to controls. Baseline measurements could be obtained for these patients, and these patients could be followed up for changes over time, either in conservative management or post-operatively. This study supports the use of OCT in the work-up and monitoring of patients with para-chiasmal lesions, but more work needs to be conducted in multi-centered prospective studies to determine the sensitivity and specificity of the modality at various OCT parameter cut-offs, as well as further analysis of macular OCT parameters.
4.6 Limitations
Not all studies looked at the same parameters, resulting in fewer studies being available for meta-analysis. Furthermore, there appears to be considerable heterogeneity in the measurements obtained through OCT. The various studies included in this paper also utilized varying machines, which have different calibrations; hence, pooling through standardized mean differences was performed, which may overestimate the absolute differences (14).
Diagnostic odds ratios could not be calculated due to the lack of studies investigating sensitivity and specificity. Hence, more studies should be performed with emphasis on sensitivity and specificity.
As different models may have different scanning speeds, technologies for segmentation, and calibrations, pooling of the measurements was conducted instead, potentially limiting generalizability.
5 Conclusion
This updated systematic review and meta-analysis of OCT provides a balanced perspective, and our analysis identifies OCT as a potentially viable tool in the evaluation, prognostication, and possibly monitoring of lesions affecting the optic chiasm.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: PubMed, Embase, SCOPUS, CINAHL, and Web of Science.
Author contributions
KL: Conceptualization, Software, Investigation, Writing – review & editing, Methodology, Validation, Formal analysis, Writing – original draft, Data curation. WTn: Investigation, Data curation, Writing – review & editing, Methodology, Software, Validation, Formal analysis, Writing – original draft. WTh: Methodology, Data curation, Conceptualization, Investigation, Formal analysis, Writing – original draft, Writing – review & editing. BL: Conceptualization, Methodology, Supervision, Investigation, Software, Writing – review & editing. CC: Supervision, Visualization, Writing – review & editing. KL: Visualization, Project administration, Formal analysis, Methodology, Validation, Conceptualization, Supervision, Writing – review & editing, Resources, Funding acquisition, Investigation. HM: Visualization, Resources, Project administration, Funding acquisition, Validation, Writing – review & editing, Supervision, Methodology.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study was funded by NIH P30 EY026877, an unrestricted grant from Research to Prevent Blindness to the Stanford Department of Ophthalmology.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author HM declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fopht.2025.1691582/full#supplementary-material
References
1. Kidd D. The optic chiasm. Handb Clin Neurol. (2011) 102:185–203. doi: 10.1016/b978-0-444-52903-9.00013-3
2. Donaldson LC, Eshtiaghi A, Sacco S, Micieli JA, and Margolin EA. Junctional scotoma and patterns of visual field defects produced by lesions involving the optic chiasm. J Neuro-Ophthalmol. (2022) 42:E203–E8. doi: 10.1097/WNO.0000000000001394
3. Wall M. Perimetry and visual field defects. Handb Clin Neurol. (2021) 178:51–77. doi: 10.1016/b978-0-12-821377-3.00003-9
4. Morgan JE, Tribble J, Fergusson J, White N, and Erchova I. The optical detection of retinal ganglion cell damage. Eye. (2017) 31:199–205. doi: 10.1038/eye.2016.290
5. Aumann S, Donner S, Fischer J, and Müller F. Optical coherence tomography (Oct): principle and technical realization. In: Bille JF, editor. High Resolution Imaging in Microscopy and Ophthalmology: New Frontiers in Biomedical Optics. Springer, Cham (CH (2019). p. 59–85.
6. Blanch RJ, Micieli JA, Oyesiku NM, Newman NJ, and Biousse V. Optical coherence tomography retinal ganglion cell complex analysis for the detection of early chiasmal compression. Pituitary. (2018) 21:515–23. doi: 10.1007/s11102-018-0906-2
7. Karimian-Jazi K. Pituitary gland tumors. Radiologe. (2019) 59:982–91. doi: 10.1007/s00117-019-0570-1
8. Famini P, Maya MM, and Melmed S. Pituitary magnetic resonance imaging for sellar and parasellar masses: ten-year experience in 2598 patients. J Clin Endocrinol Metab. (2011) 96:1633–41. doi: 10.1210/jc.2011-0168
9. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71
10. Ristić-Djurović JL, Ćirković S, Mladenović P, Romčević N, and Trbovich AM. Analysis of methods commonly used in biomedicine for treatment versus control comparison of very small samples. Comput Methods Programs Biomed. (2018) 157:153–62. doi: 10.1016/j.cmpb.2018.01.026
11. Turner RM, Bird SM, and Higgins JP. The impact of study size on meta-analyses: examination of underpowered studies in cochrane reviews. PLoS One. (2013) 8:e59202. doi: 10.1371/journal.pone.0059202
12. Rao G, Lopez-Jimenez F, Boyd J, D’Amico F, Durant NH, Hlatky MA, et al. Methodological standards for meta-analyses and qualitative systematic reviews of cardiac prevention and treatment studies: A scientific statement from the american heart association. Circulation. (2017) 136:e172–e94. doi: 10.1161/CIR.0000000000000523
13. Zhang Z, Kim HJ, Lonjon G, and Zhu Y. Balance diagnostics after propensity score matching. Ann Transl Med. (2019) 7:16. doi: 10.21037/atm.2018.12.10
14. Andrade C. Mean difference, standardized mean difference (Smd), and their use in meta-analysis: as simple as it gets. J Clin Psychiatry. (2020) 81. doi: 10.4088/JCP.20f13681
15. Higgins JP, Thompson SG, Deeks JJ, and Altman DG. Measuring inconsistency in meta-analyses. Bmj. (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557
16. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. Quadas-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. (2011) 155:529–36. doi: 10.7326/0003-4819-155-8-201110180-00009
17. Agarwal R, Jain V, Singh S, Charlotte A, Kanaujia V, Mishra P, et al. Segmented retinal analysis in pituitary adenoma with chiasmal compression: A prospective comparative study. Indian J Ophthalmol. (2021) 69:2378–84. doi: 10.4103/ijo.IJO_2086_20
18. Akdogan M, Dogan M, Beysel S, Gobeka HH, Sabaner MC, and Oran M. Optical coherence tomography angiography characteristics of the retinal and optic disc morphology in prolactinoma. Microvasc Res. (2022) 144:104424. doi: 10.1016/j.mvr.2022.104424
19. Akashi A, Kanamori A, Ueda K, Matsumoto Y, Yamada Y, and Nakamura M. The detection of macular analysis by sd-oct for optic chiasmal compression neuropathy and nasotemporal overlap. Invest Ophthalmol Vis Sci. (2014) 55:4667–72. doi: 10.1167/iovs.14-14766
20. Altun Y, Karadag AS, Yucetas SC, Saglam S, Tak AZA, Cag I, et al. Neuroretinal evaluation using optical coherence tomography in patients affected by pituitary tumors. Annali Italiani di Chirurgia. (2017) 88:7–14.
21. Batur M, Özer MD, Üçler R, Seven E, Tekin S, and Ünal F. Corneal parameters, ocular biometers, and retinal and choroidal thickness in acromegaly patients. Photodiagnosis Photodyn Ther. (2023) 44. doi: 10.1016/j.pdpdt.2023.103773
22. Bozzi MT, Mallereau CH, Todeschi J, Baloglu S, Ardellier FD, Romann J, et al. Is the oct a predictive tool to assess visual impairment in optic chiasm compressing syndrome in pituitary macroadenoma? A prospective longitudinal study. Neurosurgical Rev. (2024) 47. doi: 10.1007/s10143-024-02280-9
23. Cennamo G, Auriemma RS, Cardone D, Grasso LF, Velotti N, Simeoli C, et al. Evaluation of the retinal nerve fibre layer and ganglion cell complex thickness in pituitary macroadenomas without optic chiasmal compression. Eye (Lond). (2015) 29:797–802. doi: 10.1038/eye.2015.35
24. Cennamo G, Solari D, Montorio D, Scala MR, D’Andrea L, Tranfa F, et al. The role of oct- angiography in predicting anatomical and functional recovery after endoscopic endonasal pituitary surgery: A 1-year longitudinal study. PLoS One. (2021) 16:e0260029. doi: 10.1371/journal.pone.0260029
25. Chen YY, Li XJ, Song XY, Cong L, and Zhang YY. Microvascular changes in pituitary adenoma correlate with structural measurements and visual field loss. Neuropsychiatr Dis Treat. (2023) 19:2745–54. doi: 10.2147/ndt.S425454
26. Chou Y, Wang X, Wang Y, Gan L, Xing B, You H, et al. Early retinal microcirculation in nonfunctioning pituitary adenomas without visual field defects using optical coherence tomography angiography. J Neuroophthalmol. (2022) 42:509–17. doi: 10.1097/wno.0000000000001562
27. Chung YS, Na M, Yoo J, Kim W, Jung IH, Moon JH, et al. Optical coherent tomography predicts long-term visual outcome of pituitary adenoma surgery: new perspectives from a 5-year follow-up study. Neurosurgery. (2020) 88:106–12. doi: 10.1093/neuros/nyaa318
28. Dallorto L, Lavia C, Jeannerot AL, Shor N, Jublanc C, Boch AL, et al. Retinal microvasculature in pituitary adenoma patients: is optical coherence tomography angiography useful? Acta Ophthalmol. (2020) 98:e585–e92. doi: 10.1111/aos.14322
29. Danesh-Meyer HV, Wong A, Papchenko T, Matheos K, Stylli S, Nichols A, et al. Optical coherence tomography predicts visual outcome for pituitary tumors. J Clin Neurosci. (2015) 22:1098–104. doi: 10.1016/j.jocn.2015.02.001
30. de Araújo RB, Oyamada MK, Zacharias LC, Cunha LP, Preti RC, and Monteiro MLR. Morphological and functional inner and outer retinal layer abnormalities in eyes with permanent temporal hemianopia from chiasmal compression. Front Neurol. (2017) 8:619. doi: 10.3389/fneur.2017.00619
31. Duru N, Ersoy R, Altinkaynak H, Duru Z, Çağil N, and Çakir B. Evaluation of retinal nerve fiber layer thickness in acromegalic patients using spectral-domain optical coherence tomography. Semin Ophthalmol. (2016) 31:285–90. doi: 10.3109/08820538.2014.962165
32. Ergen A, Kaya Ergen S, Gunduz B, Subasi S, Caklili M, Cabuk B, et al. Retinal vascular and structural recovery analysis by optical coherence tomography angiography after endoscopic decompression in sellar/parasellar tumors. Sci Rep. (2023) 13. doi: 10.1038/s41598-023-40956-2
33. Garcia T, Sanchez S, Litré CF, Radoi C, Delemer B, Rousseaux P, et al. Prognostic value of retinal nerve fiber layer thickness for postoperative peripheral visual field recovery in optic chiasm compression. J Neurosurg. (2014) 121:165–9. doi: 10.3171/2014.2.Jns131767
34. Ben Ghezala I, Haddad D, Blanc J, Meillon C, Madkouri R, Borsotti F, et al. Peripapillary microvascularization analysis using swept-source optical coherence tomography angiography in optic chiasmal compression. J Ophthalmol. (2021) 2021. doi: 10.1155/2021/5531959
35. Glebauskiene B, Liutkeviciene R, Zlatkute E, Kriauciuniene L, and Zaliuniene D. Association of retinal nerve fibre layer thickness with quantitative magnetic resonance imaging data of the optic chiasm in pituitary adenoma patients. J Clin Neurosci. (2018) 50:1–6. doi: 10.1016/j.jocn.2018.01.005
36. Hernández-Echevarría O, Cuétara-Lugo EB, Pérez-Benítez MJ, González-Gómez JC, González-Diez HR, and Mendoza-Santiesteban CE. Bi-nasal sectors of ganglion cells complex and visual evoked potential amplitudes as biomarkers in pituitary macroadenoma management. Front Integr Neurosci. (2022) 16:1034705. doi: 10.3389/fnint.2022.1034705
37. Iegorova KS, Znamenska MA, Guk MO, and Mumliev AO. Early signs of primary compressive optic atrophy evidenced by oct in patients with basal brain tumors. Oftalmologicheskii Zhurnal. (2020) 1):35–9. doi: 10.31288/oftalmolzh202013539
38. Iqbal M, Irfan S, Goyal J, Singh D, Singh H, and Dutta G. An Analysis of Retinal Nerve Fiber Layer Thickness before and after Pituitary Adenoma Surgery and Its Correlation with Visual Acuity. Neurol India. (2020) 68:346–51. doi: 10.4103/0028-3886.280634
39. Jeon C, Park KA, Hong SD, Choi JW, Seol HJ, Nam DH, et al. Clinical efficacy of optical coherence tomography to predict the visual outcome after endoscopic endonasal surgery for suprasellar tumors. World Neurosurg. (2019) 132:e722–e31. doi: 10.1016/j.wneu.2019.08.031
40. Jeon H, Park KH, Kim H, and Choi H. Sd-oct parameters and visual field defect in chiasmal compression and the diagnostic value of neural network model. Eur J Ophthalmol. (2021) 31:2738–45. doi: 10.1177/1120672120947593
41. Jeon H, Suh HB, Kim TY, and Choi HY. Predictive value of oct and mri for postoperative visual recovery in patients with chiasmal compressive lesions. Eur J Ophthalmol. (2022) 32:2982–7. doi: 10.1177/11206721211073216
42. Jørstad ØK, Wigers AR, Marthinsen PB, Evang JA, and Moe MC. The value of macular optical coherence tomography in watchful waiting of suprasellar masses: A 2-year observational study. J Neuroophthalmol. (2021) 41:e516–e22. doi: 10.1097/wno.0000000000000993
43. Ju DG, Jeon C, Kim KH, Park KA, Hong SD, Seoul HJ, et al. Clinical significance of tumor-related edema of optic tract affecting visual function in patients with sellar and suprasellar tumors. World Neurosurg. (2019) 132:e862–e8. doi: 10.1016/j.wneu.2019.07.218
44. Kawaguchi T, Ogawa Y, and Tominaga T. Retinal nerve fiber layer thickness measurement for predicting visual outcome after transsphenoidal surgery: optic disc atrophy is not the deciding indicator. World Neurosurg. (2019) 127:e427–e35. doi: 10.1016/j.wneu.2019.03.143
45. Kurian DE, Rajshekhar V, Horo S, Chacko AG, Prabhu K, Mahasampath G, et al. Predictive value of retinal nerve fibre layer thickness for postoperative visual improvement in patients with pituitary macroadenoma. BMJ Open Ophthalmol. (2022) 7. doi: 10.1136/bmjophth-2021-000964
46. Lei K, Tang Y, Pang R, Zhou H, Yang L, and Wang N. Comparison of the retinal microvasculature between compressive and glaucomatous optic neuropathy. Graefe’s Arch Clin Exp Ophthalmol. (2023) 261:3589–97. doi: 10.1007/s00417-023-06137-7
47. Lang ST, Ryu WHA, Starreveld YP, and Costello FE. Good visual outcomes after pituitary tumor surgery are associated with increased visual cortex functional connectivity. J Neuroophthalmol. (2021) 41:504–11. doi: 10.1097/wno.0000000000001155
48. Lee EJ, Yang HK, Kim TW, Hwang JM, Kim YH, and Kim CY. Comparison of the pattern of retinal ganglion cell damage between patients with compressive and glaucomatous optic neuropathies. Invest Ophthalmol Visual Sci. (2015) 56:7012–20. doi: 10.1167/iovs.15-17909
49. Lee GI, Park KA, Son G, Kong DS, and Oh SY. Optical coherence tomography analysis of inner and outer retinal layers in eyes with chiasmal compression caused by suprasellar tumours. Acta Ophthalmol. (2020) 98:e373–e80. doi: 10.1111/aos.14271
50. Lee GI, Park KA, Oh SY, and Kong DS. Parafoveal and peripapillary perfusion predict visual field recovery in chiasmal compression due to pituitary tumors. J Clin Med. (2020) 9(3):697. doi: 10.3390/jcm9030697
51. Lee G-I, Park K-A, Oh SY, and Kong D-S. Analysis of optic chiasmal compression caused by brain tumors using optical coherence tomography angiography. Sci Rep. (2020) 10:2088. doi: 10.1038/s41598-020-59158-1
52. Lee GI, Park KA, Oh SY, and Kong DS. Changes in parafoveal and peripapillary perfusion after decompression surgery in chiasmal compression due to pituitary tumors. Sci Rep. (2021) 11:3464. doi: 10.1038/s41598-021-82151-1
53. Lee J, Kim SW, Kim DW, Shin JY, Choi M, Oh MC, et al. Predictive model for recovery of visual field after surgery of pituitary adenoma. J Neuro-Oncol. (2016) 130:155–64. doi: 10.1007/s11060-016-2227-5
54. Levchenko OV, Gavrilova NA, Grigoryev AY, Kalandari AA, Ioyleva EE, Gadzhieva NS, et al. Optical coherence tomography and optical coherence tomography-angiography in the diagnosis of chiasmo-sellar region compression. Vestnik Oftalmol. (2020) 136:14–22. doi: 10.17116/oftalma202013605114
55. Li X, Qin J, Cao X, Ren Z, Cui T, and Bao Y. The different structure-function correlation as measured by oct and octopus perimetry cluster analysis in intracranial tumor and glaucoma patients. Front Endocrinol. (2022) 13:938952. doi: 10.3389/fendo.2022.938952
56. Loo JL, Tian J, Miller NR, and Subramanian PS. Use of optical coherence tomography in predicting post-treatment visual outcome in anterior visual pathway meningiomas. Br J Ophthalmol. (2013) 97:1455–8. doi: 10.1136/bjophthalmol-2013-303449
57. Mambour N, Maiter D, Duprez T, Costa E, Fomekong E, Raftopoulos C, et al. Functional prognostic value of optical coherence tomography in optic chiasmal decompression: A preliminary study. J Fr Ophtalmol. (2021) 44:321–30. doi: 10.1016/j.jfo.2020.06.041
58. Mangan MS, Gelegen E, Baserer T, Gazioglu N, and Aras C. Long term predictive ability of preoperative retinal nerve fiber layer thickness in visual prognosis after chiasmal decompression surgery. Clin Neurol Neurosurg. (2021) 207:107734. doi: 10.1016/j.clineuro.2021.106734
59. Mavilio A, Sisto D, Dammacco R, Durante G, and Alessio G. Retrograde optic nerve degeneration in pituitary adenoma: A study with re-perg. Clin Ophthalmol. (2022) 16:4135–44. doi: 10.2147/OPTH.S384525
60. Mello LGM, Suzuki ACF, de Mello GR, Preti RC, Zacharias LC, and Monteiro MLR. Choroidal thickness in eyes with band atrophy of the optic nerve from chiasmal compression. J Ophthalmol. (2022) 2022:5625803. doi: 10.1155/2022/5625803
61. Meyer J, Diouf I, King J, Drummond K, Stylli S, Kaye A, et al. A comparison of macular ganglion cell and retinal nerve fibre layer optical coherence tomographic parameters as predictors of visual outcomes of surgery for pituitary tumours. Pituitary. (2022) 25:563–72. doi: 10.1007/s11102-022-01228-w
62. Mimouni M, Stiebel-Kalish H, Serov I, Chodick G, Zbedat M, and Gaton DD. Optical coherence tomography may help distinguish glaucoma from suprasellar tumor-associated optic disc. J Ophthalmol. (2019) 2019:3564809. doi: 10.1155/2019/3564809
63. Monteiro ML, Hokazono K, Cunha LP, and Oyamada MK. Correlation between multifocal pattern electroretinography and fourier-domain oct in eyes with temporal hemianopia from chiasmal compression. Graefes Arch Clin Exp Ophthalmol. (2013) 251:903–15. doi: 10.1007/s00417-012-2156-8
64. Monteiro MLR, Hokazono K, Fernandes DB, Costa-Cunha LVF, Sousa RM, Raza AS, et al. Evaluation of inner retinal layers in eyes with temporal hemianopic visual loss from chiasmal compression using optical coherence tomography. Invest Ophthalmol Visual Sci. (2014) 55:3328–36. doi: 10.1167/iovs.14-14118
65. Monteiro MLR, Costa-Cunha LVF, Cunha LP, and Malta RFS. Correlation between macular and retinal nerve fibre layer fourier-domain oct measurements and visual field loss in chiasmal compression. Eye. (2010) 24:1382–90. doi: 10.1038/eye.2010.48
66. Moon CH, Hwang SC, Ohn YH, and Park TK. The time course of visual field recovery and changes of retinal ganglion cells after optic chiasmal decompression. Invest Ophthalmol Visual Sci. (2011) 52:7966–73. doi: 10.1167/iovs.11-7450
67. Moon CH, Hwang SC, Kim BT, Ohn YH, and Park TK. Visual prognostic value of optical coherence tomography and photopic negative response in chiasmal compression. Invest Ophthalmol Visual Sci. (2011) 52:8527–33. doi: 10.1167/iovs.11-8034
68. Moura FC, Costa-Cunha LV, Malta RF, and Monteiro ML. Relationship between visual field sensitivity loss and quadrantic macular thickness measured with stratus-optical coherence tomography in patients with chiasmal syndrome. Arq Bras Oftalmol. (2010) 73:409–13. doi: 10.1590/s0004-27492010000500004
69. Nair SS, Varsha AS, Hegde A, Raju B, Nayak R, Menon G, et al. Correlation of pre-operative and post-operative retinal nerve fibre layer thickness with visual outcome following decompression of pituitary macroadenoma. Clin Neurol Neurosurg. (2024) 244:108446. doi: 10.1016/j.clineuro.2024.108446
70. Nakamura M, Ishikawa-Tabuchi K, Kanamori A, Yamada Y, and Negi A. Better performance of rtvue than cirrus spectral-domain optical coherence tomography in detecting band atrophy of the optic nerve. Graefes Arch Clin Exp Ophthalmol. (2012) 250:1499–507. doi: 10.1007/s00417-012-2095-4
71. Ogmen BE, Ugurlu N, Faki S, Polat SB, Ersoy R, and Cakir B. Retinal layers in prolactinoma patients: A spectral-domain optical coherence tomography study. Int Ophthalmol. (2021) 41:1373–9. doi: 10.1007/s10792-021-01701-8
72. Ohkubo S, Higashide T, Takeda H, Murotani E, Hayashi Y, and Sugiyama K. Relationship between macular ganglion cell complex parameters and visual field parameters after tumor resection in chiasmal compression. Jpn J Ophthalmol. (2012) 56:68–75. doi: 10.1007/s10384-011-0093-4
73. Özcan Y, Kayiran A, Kelestimur F, Ekinci G, and Türe U. Changes in the peripapillary and subfoveal choroidal vascularity index after transsphenoidal surgery for pituitary macroadenoma. Int Ophthalmol. (2022) 42:3691–702. doi: 10.1007/s10792-022-02366-7
74. Phal PM, Steward C, Nichols AD, Kokkinos C, Desmond PM, Danesh-Meyer H, et al. Assessment of optic pathway structure and function in patients with compression of the optic chiasm: A correlation with optical coherence tomography. Invest Ophthalmol Visual Sci. (2016) 57:3884–90. doi: 10.1167/iovs.15-18734
75. Pang Y, Tan Z, Mo W, Chen X, Wei J, Guo Q, et al. A pilot study of combined optical coherence tomography and diffusion tensor imaging method for evaluating microstructural change in the visual pathway of pituitary adenoma patients. BMC Ophthalmol. (2022) 22:115. doi: 10.1186/s12886-022-02320-2
76. Pang Y, Tan Z, Chen X, Liao Z, Yang X, Zhong Q, et al. Evaluation of preoperative visual pathway impairment in patients with non-functioning pituitary adenoma using diffusion tensor imaging coupled with optical coherence tomography. Front Neurosci. (2023) 17:1057781. doi: 10.3389/fnins.2023.1057781
77. Pang Y, Zhao Q, Huang Z, Lu K, Zhou F, Mo W, et al. Visual pathway recovery post pituitary adenoma surgery: insights from retinal structure, vascular density, and neural conduction analysis. Ophthalmol Ther. (2024) 13:1993–2008. doi: 10.1007/s40123-024-00966-3
78. Park SH, Kang MS, Kim SY, Lee JE, Shin JH, Choi H, et al. Analysis of factors affecting visual field recovery following surgery for pituitary adenoma. Int Ophthalmol. (2021) 41:2019–26. doi: 10.1007/s10792-021-01757-6
79. Pekel G, Akin F, Ertürk MS, Acer S, Yagci R, Hıraali MC, et al. Chorio-retinal thickness measurements in patients with acromegaly. Eye (Lond). (2014) 28:1350–4. doi: 10.1038/eye.2014.216
80. Póczoš P, Kremláček J, Česák T, Macháčková M, and Jirásková N. The use of optical coherence tomography in chiasmal compression. Ceska Slovenska Oftalmol. (2019) 75:120–7. doi: 10.31348/2019/3/2
81. Poczos P, Česák T, Jirásková N, Macháčková M, Čelakovský P, Adamkov J, et al. Optical coherence tomography and visual evoked potentials in evaluation of optic chiasm decompression. Sci Rep. (2022) 12:2102. doi: 10.1038/s41598-022-06097-8
82. Qiao N, Ye Z, Shou X, Wang Y, Li S, Wang M, et al. Discrepancy between structural and functional visual recovery in patients after trans-sphenoidal pituitary adenoma resection. Clin Neurol Neurosurg. (2016) 151:9–17. doi: 10.1016/j.clineuro.2016.09.005
83. Qiao ND, Ye Z, Shen M, Shou XF, Wang YF, Li SQ, et al. Retinal nerve fiber layer changes after transsphenoidal and transcranial pituitary adenoma resection. Pituitary. (2016) 19:75–81. doi: 10.1007/s11102-015-0689-7
84. Rudman Y, Duskin-Bitan H, Masri-Iraqi H, Akirov A, and Shimon I. Visual morbidity in macroprolactinoma: A retrospective cohort study. Clin Endocrinol. (2024) 101(6):648–58. doi: 10.1111/cen.15120
85. Şahin M, Şahin A, Kılınç F, Yüksel H, Özkurt ZG, Türkcü FM, et al. Retina ganglion cell/inner plexiform layer and peripapillary nerve fiber layer thickness in patients with acromegaly. Int Ophthalmol. (2017) 37:591–8. doi: 10.1007/s10792-016-0310-8
86. Santorini M, De Moura TF, Barraud S, Litré CF, Brugniart C, Denoyer A, et al. Comparative evaluation of two sd-oct macular parameters (Gcc, gcl) and rnfl in chiasmal compression. Eye Brain. (2022) 14:35–48. doi: 10.2147/EB.S337333
87. Sasagawa Y, Nakahara M, Takemoto D, and Nakada M. Optical coherence tomography detects early optic nerve damage before visual field defect in patients with pituitary tumors. Neurosurgical Rev. (2023) 46(1):85. doi: 10.1007/s10143-023-01990-w
88. Saxena R, Gopalakrishnan K, Singh D, Mahapatra AK, and Menon V. Retinal nerve fiber layer changes: A predictor of visual function recovery in pituitary adenomas. Indian J Med Specialities. (2015) 6:141–5. doi: 10.1016/j.injms.2015.07.004
89. Shinohara Y, Todokoro D, Yamaguchi R, Tosaka M, Yoshimoto Y, and Akiyama H. Retinal ganglion cell analysis in patients with sellar and suprasellar tumors with sagittal bending of the optic nerve. Sci Rep. (2022) 12:11092. doi: 10.1038/s41598-022-15381-6
90. Singha S, Beniwal M, Mailankody P, Battu R, Saini J, Tyagi G, et al. Role of optical coherence tomography in predicting visual outcome after surgery for sellar and supra-sellar tumors. Neurol India. (2024) 72:50–7. doi: 10.4103/neurol-India.Neurol-India-D-23-00654
91. Sousa RM, Oyamada MK, Cunha LP, and Monteiro MLR. Multifocal visual evoked potential in eyes with temporal hemianopia from chiasmal compression: correlation with standard automated perimetry and oct findings. Invest Ophthalmol Visual Sci. (2017) 58:4436–46. doi: 10.1167/iovs.17-21529
92. Suh H, Choi H, and Jeon H. The radiologic characteristics and retinal thickness are correlated with visual field defect in patients with a pituitary mass. J Neuro-Ophthalmol. (2021) 41:E541–E7. doi: 10.1097/WNO.0000000000001011
93. Sun M, Zhang H, Chen X, and Zhang Q. Quantitative analysis of macular retina using light reflection indices derived from sd-oct for pituitary adenoma. J Ophthalmol. (2020) 2020:8896114. doi: 10.1155/2020/8896114
94. Sun M, Zhang Z, Ma C, Chen S, and Chen X. Quantitative analysis of retinal layers on three-dimensional spectral-domain optical coherence tomography for pituitary adenoma. PloS One. (2017) 12:e0179532. doi: 10.1371/journal.pone.0179532
95. Suzuki ACF, Zacharias LC, Preti RC, Cunha LP, and Monteiro MLR. Circumpapillary and macular vessel density assessment by optical coherence tomography angiography in eyes with temporal hemianopia from chiasmal compression. Correlation Retinal Neural Visual Field Loss Eye (Lond). (2020) 34:695–703. doi: 10.1038/s41433-019-0564-2
96. Tang Y, Liang X, Xu J, Wang K, and Jia W. The value of optical coherence tomography angiography in pituitary adenomas. J Integr Neurosci. (2022) 21:142. doi: 10.31083/j.jin2105142
97. Tang Y, Jia W, Xue Z, Yuan L, Qu Y, Yang L, et al. Prognostic value of radial peripapillary capillary density for visual field outcomes in pituitary adenoma: A case-control study. J Clin Neurosci. (2022) 100:113–9. doi: 10.1016/j.jocn.2022.04.012
98. Thammakumpee K, Buddawong J, Vanikieti K, Jindahra P, and Padungkiatsagul T. Preoperative peripapillary retinal nerve fiber layer thickness as the prognostic factor of postoperative visual functions after endoscopic transsphenoidal surgery for pituitary adenoma. Clin Ophthalmol. (2022) 16:4191–8. doi: 10.2147/opth.S392987
99. Tieger MG, Hedges TR 3rd, Ho J, Erlich-Malona NK, Vuong LN, Athappilly GK, et al. Ganglion cell complex loss in chiasmal compression by brain tumors. J Neuroophthalmol. (2017) 37:7–12. doi: 10.1097/wno.0000000000000424
100. Ueda K, Kanamori A, Akashi A, Matsumoto Y, Yamada Y, and Nakamura M. Evaluation of the distribution pattern of the circumpapillary retinal nerve fibre layer from the nasal hemiretina. Br J Ophthalmol. (2015) 99:1419–23. doi: 10.1136/bjophthalmol-2014-306100
101. Wang G, Gao J, Yu W, Li Y, and Liao R. Changes of peripapillary region perfusion in patients with chiasmal compression caused by sellar region mass. J Ophthalmol. (2021) 2021:5588077. doi: 10.1155/2021/5588077
102. Wang MTM, King J, Symons RCA, Stylli SS, Meyer J, Daniell MD, et al. Prognostic utility of optical coherence tomography for long-term visual recovery following pituitary tumor surgery. Am J Ophthalmol. (2020) 218:247–54. doi: 10.1016/j.ajo.2020.06.004
103. Wang MTM, King J, Symons RCA, Stylli SS, Daniell MD, Savino PJ, et al. Temporal patterns of visual recovery following pituitary tumor resection: A prospective cohort study. J Clin Neurosci. (2021) 86:252–9. doi: 10.1016/j.jocn.2021.01.007
104. Wang X, Chou Y, Zhu H, Xing B, Yao Y, Lu L, et al. Retinal microvascular alterations detected by optical coherence tomography angiography in nonfunctioning pituitary adenomas. Transl Vis Sci Technol. (2022) 11:5. doi: 10.1167/tvst.11.1.5
105. Xia L, Wenhui J, Xiaowen Y, Wenfang X, Wei Z, Yanjun H, et al. Predictive value of macular ganglion cell-inner plexiform layer thickness in visual field defect of pituitary adenoma patients: A case-control study. Pituitary. (2022) 25:667–72. doi: 10.1007/s11102-022-01248-6
106. Yang L, Qu Y, Lu W, and Liu F. Evaluation of macular ganglion cell complex and peripapillary retinal nerve fiber layer in primary craniopharyngioma by fourier-domain optical coherence tomography. Med Sci Monitor. (2016) 22:2309–14. doi: 10.12659/MSM.896221
107. Yoneoka Y, Hatase T, Watanabe N, Jinguji S, Okada M, Takagi M, et al. Early morphological recovery of the optic chiasm is associated with excellent visual outcome in patients with compressive chiasmal syndrome caused by pituitary tumors. Neurol Res. (2015) 37:1–8. doi: 10.1179/1743132814Y.0000000407
108. Yoo YJ, Hwang JM, Yang HK, Joo JD, Kim YH, and Kim CY. Prognostic value of macular ganglion cell layer thickness for visual outcome in parasellar tumors. J Neurol Sci. (2020) 414:116823. doi: 10.1016/j.jns.2020.116823
109. Yum HR, Park SH, Park HY, and Shin SY. Macular ganglion cell analysis determined by cirrus hd optical coherence tomography for early detecting chiasmal compression. PloS One. (2016) 11:e0153064. doi: 10.1371/journal.pone.0153064
110. Moon JS and Shin SY. Segmented retinal layer analysis of chiasmal compressive optic neuropathy in pituitary adenoma patients. Graefes Arch Clin Exp Ophthalmol. (2020) 258:419–25. doi: 10.1007/s00417-019-04560-3
111. Orman G, Sungur G, and Culha C. Assessment of inner retina layers thickness values in eyes with pituitary tumours before visual field defects occur. Eye (Lond). (2021) 35:1159–64. doi: 10.1038/s41433-020-1032-8
112. Cennamo G, Solari D, Montorio D, Scala MR, Melenzane A, Fossataro F, et al. Early vascular modifications after endoscopic endonasal pituitary surgery: the role of oct-angiography. PloS One. (2020) 15:e0241295. doi: 10.1371/journal.pone.0241295
113. Jeong SS, Funari A, and Agarwal V. Diagnostic and prognostic utility of optical coherence tomography in patients with sellar/suprasellar lesions with chiasm impingement: A systematic review/meta-analyses. World Neurosurg. (2022) 162:163. doi: 10.1016/j.wneu.2022.03.011
114. Chou Y, Zhang B, Gan L, Ma J, and Zhong Y. Clinical efficacy of optical coherence tomography in sellar mass lesions: A meta-analysis. Pituitary. (2020) 23:733–44. doi: 10.1007/s11102-020-01072-w
115. Testoni PA. Optical coherence tomography. ScientificWorldJournal. (2007) 7:87–108. doi: 10.1100/tsw.2007.29
116. Costello F. Optical coherence tomography in neuro-ophthalmology. Neurol Clin. (2017) 35:153–63. doi: 10.1016/j.ncl.2016.08.012
117. Sotirchos ES and Saidha S. Oct is an alternative to mri for monitoring ms - yes. Mult Scler. (2018) 24:701–3. doi: 10.1177/1352458517753722
118. Chan VTT, Sun Z, Tang S, Chen LJ, Wong A, Tham CC, et al. Spectral-domain oct measurements in alzheimer’s disease: A systematic review and meta-analysis. Ophthalmology. (2019) 126:497–510. doi: 10.1016/j.ophtha.2018.08.009
119. Lin L and Aloe AM. Evaluation of various estimators for standardized mean difference in meta-analysis. Stat Med. (2021) 40:403–26. doi: 10.1002/sim.8781
120. Takeshima N, Sozu T, Tajika A, Ogawa Y, Hayasaka Y, and Furukawa TA. Which is more generalizable, powerful and interpretable in meta-analyses, mean difference or standardized mean difference? BMC Med Res Methodol. (2014) 14:30. doi: 10.1186/1471-2288-14-30
121. Tan CSH, Li KZ, and Lim TH. A novel technique of adjusting segmentation boundary layers to achieve comparability of retinal thickness and volumes between spectral domain and time domain optical coherence tomography. Invest Ophthalmol Visual Sci. (2012) 53:5515–9. doi: 10.1167/iovs.12-9868
122. Strouthidis NG, Vinciotti V, Tucker AJ, Gardiner SK, Crabb DP, and Garway-Heath DF. Structure and function in glaucoma: the relationship between a functional visual field map and an anatomic retinal map. Invest Ophthalmol Visual Sci. (2006) 47:5356–62. doi: 10.1167/iovs.05-1660
123. Reznicek L, Seidensticker F, Mann T, Hübert I, Buerger A, Haritoglou C, et al. Correlation between peripapillary retinal nerve fiber layer thickness and fundus autofluorescence in primary open-angle glaucoma. Clin Ophthalmol. (2013) 7:1883–8. doi: 10.2147/opth.S49112
124. Garway-Heath DF, Poinoosawmy D, Fitzke FW, and Hitchings RA. Mapping the visual field to the optic disc in normal tension glaucoma eyes11the authors have no proprietary interest in the development or marketing of any product or instrument mentioned in this article. Ophthalmology. (2000) 107:1809–15. doi: 10.1016/S0161-6420(00)00284-0
125. Pawar PR, Booth J, Neely A, McIlwaine G, and Lueck CJ. Nerve fibre organisation in the human optic nerve and chiasm: what do we really know? Eye (Lond). (2024) 38:2457–71. doi: 10.1038/s41433-024-03137-7
126. Fitzgibbon T and Taylor SF. Retinotopy of the human retinal nerve fibre layer and optic nerve head. J Comp Neurol. (1996) 375:238–51. doi: 10.1002/(sici)1096-9861(19961111)375:2<238::Aid-cne5>3.0.Co;2-3
127. Danesh-Meyer HV, Papchenko T, Savino PJ, Law A, Evans J, and Gamble GD. In vivo retinal nerve fiber layer thickness measured by optical coherence tomography predicts visual recovery after surgery for parachiasmal tumors. Invest Ophthalmol Vis Sci. (2008) 49:1879–85. doi: 10.1167/iovs.07-1127
Keywords: pituitary, parachiasmal neoplasm, optical coherence tomography, retinal nerve fiber layer, ganglion cell layer, ganglion cell complex
Citation: Lim KSA, Tng WXA, Theng WDB, Lee BTK, Chin CF, Li KZ and Moss HE (2026) The role of optical coherence tomography in the evaluation of para-chiasmal lesions: a systematic review and meta-analysis. Front. Ophthalmol. 5:1691582. doi: 10.3389/fopht.2025.1691582
Received: 24 August 2025; Accepted: 09 December 2025; Revised: 26 October 2025;
Published: 26 January 2026.
Edited by:
Saif Aldeen Alryalat, University of Illinois Chicago, United StatesReviewed by:
Raed Behbehani, Al Bahar Eye Center, KuwaitMostafa Algabri, University of Baghdad, Iraq
Copyright © 2026 Lim, Tng, Theng, Lee, Chin, Li and Moss. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kelvin Zhenghao Li, a2VsdmluLmxpemhAbnR1LmVkdS5zZw==
Chee Fang Chin3