Structural and Functional Predictors of Visual Recovery in Chiasmal Compression Due to Sellar Tumors: Baseline MD, RNFL, and GCIPL

Structural and Functional Predictors of Visual Recovery in Chiasmal Compression Due to Sellar Tumors: Baseline MD, RNFL, and GCIPL | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Structural and Functional Predictors of Visual Recovery in Chiasmal Compression Due to Sellar Tumors: Baseline MD, RNFL, and GCIPL Mariana Vaz, João Vaz, Sara Simões Dias, Amets Sagarribay, Daniela Dias, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8418543/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract INTRODUCTION Optic neuropathy from sellar and suprasellar tumors is a frequent and potentially reversible cause of visual dysfunction. Predicting postoperative recovery is clinically relevant for surgical planning and patient counseling. The prognostic significance of regional OCT alterations remains unestablished. This study aimed to evaluate whether OCT quadrants predict postoperative visual recovery, and to determine which structural biomarkers independently contribute to prognosis. METHODS This retrospective study included patients with optic neuropathy secondary to sellar or suprasellar lesions surgically treated up to January 2025. Pre- and postoperative ophthalmologic evaluation included automated perimetry and spectral-domain OCT, and follow-up between 6-24 months. OCT parameters included global and sectoral RNFL and mGCIPL thickness. Random-effects hierarchical regression models were applied to account for within-patient correlation. Univariable models were performed for each covariate, followed by multivariable models incorporating baseline MD and one OCT parameter. Statistical analyses were performed using a significance level of 0.05. RESULTS Twenty-eight patients were included. In univariable analyses, neither age nor radiological evidence of chiasmal compression were significant predictors of postoperative visual outcome. Baseline MD showed a strong association with postoperative outcome (p < 0.001). All RNFL and mGCIPL sectors correlated significantly with follow-up MD (all p < 0.001). In multivariable models, baseline MD retained significance when combined with RNFL parameters, but lost significance when combined with macular GCIPL measures. RNFL parameters did not retain significance after adjustment. In contrast, three mGCIPL sectors retained independent prognostic value: nasal-superior (p = 0.014), superior (p = 0.027), and inferior (p < 0.001). DISCUSSION AND CONCLUSION Baseline MD was the most robust determinant of visual recovery after chiasmal decompression. Three GCIPL sectors, nasal-superior, superior, and inferior, emerged as independent predictors of postoperative outcome. Nasal-superior thickness highlights the vulnerability of decussating fibers at the chiasm. RNFL parameters did not add value beyond baseline MD, reflecting redundancy and possible floor effects. Clinically, sectoral macular OCT analysis should complement functional testing in preoperative evaluation. KEY MESSAGE What is known? • Preoperative visual field impairment, particularly baseline mean deviation (MD), is a major determinant of postoperative visual recovery in patients with chiasmal compression due to sellar tumors. • Optical coherence tomography (OCT)–derived structural parameters, including peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell–inner plexiform layer (GCIPL) thickness, are associated with functional outcomes in compressive optic neuropathies. What is new? • Specific macular GCIPL sectors (nasal-superior, superior, and inferior) demonstrate independent prognostic value for postoperative visual recovery beyond baseline MD. • Peripapillary RNFL parameters do not provide additional independent prognostic information after adjustment for baseline functional deficit. • Sectoral macular OCT analysis may enhance preoperative risk stratification and prognostic accuracy in patients undergoing chiasmal decompression. INTRODUCTION Optic neuropathy resulting from sellar and suprasellar masses, most commonly pituitary adenomas, represents a frequent and potentially reversible cause of visual dysfunction. 1 , 2 Pituitary tumors account for approximately 12–15% of all intracranial neoplasms, and up to 70% of patients exhibit visual field deficits at the time of diagnosis. 3 , 4 The characteristic pattern of visual impairment, classically bitemporal hemianopia, arises from preferential damage to the decussating nasal retinal ganglion cell axons within the optic chiasm. 5 Surgical decompression of the chiasm often leads to partial or complete visual recovery; however, a significant proportion of patients experience incomplete restoration of function, despite technically successful interventions. 6 The ability to accurately predict visual outcomes following decompression is of substantial clinical importance. Reliable preoperative prognostic markers may aid in surgical planning, inform patient counseling, and assist in the optimization of treatment timing. Conventional predictors such as duration of symptoms, preoperative visual acuity or field loss, and tumor size have demonstrated limited prognostic consistency. 7 – 9 In this context, structural neuro-ophthalmic imaging techniques have garnered attention for their potential to objectively assess the integrity of the anterior visual pathway. Optical coherence tomography (OCT) has emerged as a valuable, non-invasive imaging modality for the evaluation of the optic nerve head and retinal architecture. Quantitative parameters derived from OCT, particularly the peripapillary retinal nerve fiber layer (RNFL) and the macular ganglion cell-inner plexiform layer (mGCIPL), have shown strong correlations with functional visual outcomes in compressive optic neuropathies. Several studies have demonstrated that preserved global RNFL thickness prior to surgical intervention is associated with more favorable postoperative visual recovery. 10 – 12 These findings reflect the value of RNFL thickness as a surrogate marker of axonal integrity and irreversible neuronal loss. Despite the utility of global RNFL metrics, the prognostic relevance of regional RNFL and mGCIPL alterations remains underexplored. These regions are anatomically congruent with the fibers most susceptible to compressive injury at the level of the optic chiasm. Damage to the nasal quadrant, in particular, may more accurately reflect early or localized axonal compromise in the context of chiasmal compression. As such, a more granular analysis of quadrant-specific thickness could provide improved predictive value compared to global averages. The present study aims to investigate the predictive capacity of preoperative RNFL and mGCIPL thickness, as measured by OCT, for visual recovery following chiasmal decompression surgery. By focusing on these anatomically relevant sectors, this study seeks to identify objective, region-specific structural biomarkers that may enhance the accuracy of prognostic models, refine clinical decision-making, and ultimately contribute to improved patient outcomes in the management of compressive optic neuropathies. METHODS This retrospective observational study was conducted at a tertiary care center with multidisciplinary clinical support in neuroophthalmology, neurosurgery and endocrinology. The protocol adhered to the Declaration of Helsinki and received approval from the institutional ethics committee. The medical records of patients with optic neuropathy due to pituitary tumors treated until January 2025 were reviewed. Eligible participants comprised adults (≥ 18 years) who had undergone surgical resection of a pituitary tumor and for whom pre- and postoperative ophthalmic evaluations were available, including standard automated perimetry and spectral-domain optical coherence tomography (SD-OCT). Postoperative assessments were conducted within 6–24 months following surgery. Patients were excluded if they had concomitant eye diseases that could affect OCT or visual field results, such as glaucoma, diabetic retinopathy or optic neuritis, or if they had undergone unrelated neurosurgical procedures. Visual field tests were considered unreliable and excluded if they contained more than 25% fixation loss, false-positive or false-negative results. OCT scans were excluded if the signal quality was insufficient or the segmentation was inaccurate. Functional assessment consisted of standard automated perimetry using the Haag-Streit Octopus 900 perimeter, with mean deviation (MD) used to quantify visual field performance. Structural assessment was performed using the Heidelberg Spectralis® SD-OCT system. Measurements of peripapillary retinal nerve fiber layer (RNFL) thickness and macular ganglion cell inner plexiform layer (mGCIPL) thickness were analyzed. The primary outcome was postoperative visual field status, quantified by mean deviation (MD). Statistical analyses were performed in IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) and Stata (version XX; StataCorp, College Station, TX, USA). Descriptive statistics summarized demographic, clinical, structural, and functional variables. Group comparisons and simple associations were conducted using parametric or non-parametric methods, as appropriate. To evaluate predictors of postoperative mean deviation (MD), we conducted hierarchical random-effects regression models to account for within-patient correlation. Univariable models were first estimated for each covariate; multivariable models then incorporated baseline MD together with a single OCT parameter (RNFL or mGCIPL). All statistical analysis were done for a significance level of 0.05. RESULTS The demographic and clinical characteristics of the cohort are summarized in Table 1 . Twenty-eight patients were included (17 females, 60.7%; 11 males, 39.3%), with a mean age of 60.6 ± 11.8 years (range, 38–78). Non-functioning adenomas predominated (50.0%), followed by somatotroph adenomas (21.4%) and gonadotroph adenomas (7.1%); the remaining 21.4% comprised other histopathological entities as detailed in Table 1 . Radiological evidence of chiasmal compression was documented in 57.1% of cases. For the regression analyses, both eyes were incorporated whenever the corresponding examinations were available and analyzable. Hierarchical random-effects models were employed to account for intra-individual (two-eye) correlation. Because several patients lacked valid results for one or more examinations, the number of observations varied across models, reflecting differences in the availability of reliable perimetric and OCT data. Table 1 Demographic and Clinical Characteristics of the Sample Variable n (%) / Mean ± SD Range Age (years) 60.6 ± 11.8 38–78 Sex – Female 17 (60.7%) - – Male 11 (39.3%) - Tumor type – Non-functioning adenoma 14 (50.0%) - – Somatotroph adenoma 6 (21.4%) - – Gonadotroph adenoma 2 (7.1%) - – Others* 6 (21.4%) - MRI evidence of chiasmal compression – Present 16 (57.1%) - – Absent 12 (42.9%) - Values are expressed as mean ± standard deviation (SD) or number (percentage). MRI = magnetic resonance imaging. “Others” tumor types include tuberculum sellae meningioma, T-cell lymphoma, hemangioma, neurocytoma, neuroendocrine tumor, and Cushing’s disease. In univariable analyses (Table 2 ), age (β = 0.062; 95% CI, -0.248 to 0.372; p = 0.694) and MRI evidence of chiasmal compression (β = 2.568; 95% CI, -1.630 to 6.760; p = 0.230) were not associated with postoperative visual outcomes. By contrast, baseline mean deviation (MD) demonstrated a robust positive association with follow-up MD (β = 0.410; 95% CI, 0.250–0.570; p < 0.001). Structural OCT parameters were likewise associated with outcome in univariable models: global RNFL thickness (β = -0.238; 95% CI, -0.320 to -0.160; p < 0.001) and all RNFL sectors, as well as global and sectoral mGCIPL measures, were significant (all p < 0.001). Table 2 Univariable hierarchical regression models for predictors of follow-up MD Predictor β (Coef.) 95% CI p-value Age 0.062 -0.248 to 0.372 0.694 MRI compression 2.568 -1.630 to 6.760 0.230 Baseline MD 0.410 0.250 to 0.570 < 0.001 RNFL Global -0.238 -0.320 to -0.160 < 0.001 RNFL Nasal-Superior -0.080 -0.140 to -0.020 0.007 RNFL Temporal-Superior -0.080 -0.130 to -0.030 0.002 RNFL Temporal-Inferior -0.100 -0.140 to -0.050 < 0.001 RNFL Nasal -0.180 -0.260 to -0.100 < 0.001 RNFL Temporal -0.200 -0.310 to -0.080 0.001 mGCIPL Global -0.376 -0.480 to -0.270 < 0.001 mGCIPL Nasal-Superior -0.240 -0.360 to -0.120 < 0.001 mGCIPL Nasal-Inferior -0.300 -0.450 to -0.160 < 0.001 mGCIPL Temporal-Superior -0.250 -0.370 to -0.130 < 0.001 mGCIPL Temporal-Inferior -0.250 -0.350 to -0.160 < 0.001 mGCIPL Superior -0.320 -0.440 to -0.200 < 0.001 mGCIPL Inferior -0.380 -0.500 to -0.270 < 0.001 Coefficients (β) are shown with 95% confidence intervals (CI) and p-values. Statistically significant results (p < 0.05) are presented in bold. MD = mean deviation; RNFL = retinal nerve fiber layer; mGCIPL = macular ganglion cell-inner plexiform layer; MRI = magnetic resonance imaging. In multivariable analyses (Table 3 ), baseline MD remained an independent predictor in all models that incorporated RNFL parameters and in selected mGCIPL models (superior and inferior). RNFL parameters did not retain statistical significance after adjustment for baseline MD. In contrast, three macular sectors preserved independent prognostic value: nasal-superior mGCIPL (β = -0.110; 95% CI, -0.190 to -0.020; p = 0.014), superior mGCIPL (β = -0.140; 95% CI, -0.260 to -0.020; p = 0.027), and inferior mGCIPL (β = -0.380; 95% CI, -0.500 to -0.270; p < 0.001). The remaining macular sectors and global mGCIPL did not retain significance after adjustment. Table 3 Multivariable hierarchical regression models including baseline MD and OCT parameters Model / Predictor β (Coef.) 95% CI p-value MD + RNFL Global: MD 0.310 0.120 to 0.500 0.002 MD + RNFL Global: RNFL Global -0.090 -0.210 to 0.020 0.117 MD + RNFL Nasal-Superior: MD 0.420 0.230 to 0.600 < 0.001 MD + RNFL Nasal-Superior: RNFLNS 0.000 -0.050 to 0.060 0.890 MD + RNFL Nasal-Inferior: MD 0.360 0.180 to 0.540 < 0.001 MD + RNFL Nasal-Inferior: RNFLNI -0.040 -0.110 to 0.030 0.243 MD + RNFL Temporal-Superior: MD 0.370 0.210 to 0.520 < 0.001 MD + RNFL Temporal-Superior: RNFLTS -0.040 -0.090 to 0.010 0.114 MD + RNFL Temporal-Inferior: MD 0.380 0.210 to 0.560 < 0.001 MD + RNFL Temporal-Inferior: RNFLTI -0.020 -0.070 to 0.040 0.510 MD + RNFL Nasal: MD 0.340 0.150 to 0.540 0.001 MD + RNFL Nasal: RNFLN -0.060 -0.160 to 0.040 0.228 MD + RNFL Temporal: MD 0.370 0.210 to 0.520 < 0.001 MD + RNFL Temporal: RNFLT -0.080 -0.190 to 0.030 0.146 MD + mGCIPL Global: MD 0.050 -0.090 to 0.200 0.472 MD + mGCIPL Global: CCGG -0.140 -0.310 to 0.020 0.086 MD + mGCIPL Nasal-Superior: MD 0.060 -0.070 to 0.190 0.353 MD + mGCIPL Nasal-Superior: CCGNS -0.110 -0.190 to -0.020 0.014 MD + mGCIPL Nasal-Inferior: MD 0.070 -0.090 to 0.220 0.398 MD + mGCIPL Nasal-Inferior: CCGNI -0.100 -0.240 to 0.050 0.184 MD + mGCIPL Temporal-Superior: MD 0.060 -0.070 to 0.190 0.353 MD + mGCIPL Temporal-Superior: CCGTS -0.110 -0.270 to 0.050 0.461 MD + mGCIPL Temporal-Inferior: MD 0.070 -0.090 to 0.220 0.398 MD + mGCIPL Temporal-Inferior: CCGTI -0.100 -0.240 to 0.050 0.200 MD + mGCIPL Superior: MD 0.350 0.150 to 0.540 0.001 MD + mGCIPL Superior: CCGS -0.140 -0.260 to -0.020 0.027 MD + mGCIPL Inferior: MD 0.370 0.210 to 0.560 < 0.001 MD + mGCIPL Inferior: CCGI -0.380 -0.500 to -0.270 < 0.001 Each model includes baseline MD and one structural parameter. Coefficients (β), 95% confidence intervals (CI), and p-values are reported. Statistically significant results (p < 0.05) are presented in bold. MD = mean deviation; RNFL = retinal nerve fiber layer; mGCIPL = macular ganglion cell-inner plexiform layer; OCT = optical coherence tomography. DISCUSSION In this cohort of patients with pituitary-region tumors undergoing neurosurgical chiasmal decompression, baseline visual field status, quantified by MD, emerged as the principal determinant of postoperative visual outcome, whereas sectoral macular structure provided additional, independent prognostic information. After adjustment for baseline MD, three mGCIPL sectors, nasal-superior, superior, and inferior, retained statistically significant associations with postoperative MD, while peripapillary RNFL parameters did not. These findings suggest that macular indices of ganglion-cell integrity capture residual neuronal reserve not entirely reflected by functional measures, whereas RNFL thickness conveys overlapping and, therefore, less discriminative prognostic information once baseline MD is considered. The sectoral distribution of these associations is anatomically and pathophysiologically consistent. The nasal-superior macular region contains ganglion-cell axons that decussate at the optic chiasm and are therefore particularly vulnerable to compressive lesions. 5 Conversely, the superior and inferior macular sectors correspond to fibers subserving the inferior and superior visual fields, respectively, regions of critical importance for central and paracentral vision. Preservation of these sectors likely reflects structural integrity of the retinal ganglion-cell populations most relevant for visual function and recovery potential. The persistence of these sectors as independent predictors supports the concept that sectoral macular OCT provides a more refined assessment of clinically meaningful structural damage than global macular or peripapillary metrics. Previous literature has underscored the prognostic relevance of OCT-derived measures in chiasmal compression. Danesh-Meyer et al. 13 demonstrated that greater preoperative peripapillary RNFL thickness is associated with superior postoperative visual recovery, establishing a structural-functional link in this setting. Subsequent studies reported that macular ganglion-cell metrics are particularly informative: macular GCIPL analysis facilitates early detection of chiasmal damage and correlates with postoperative function, with several cohorts highlighting sectoral associations in the nasal and superior regions 14 . The present findings are concordant with this body of evidence, indicating that sectoral mGCIPL thickness, notably the nasal-superior, superior, and inferior sectors, provides prognostically relevant information beyond baseline perimetric status, whereas RNFL measures do not retain independent significance after adjustment. From a clinical standpoint, these results support a hierarchical framework for prognostication in compressive optic neuropathy secondary to pituitary adenoma. Baseline MD should remain the reference parameter for estimating postoperative outcome, while sectoral macular OCT, especially the nasal-superior, superior, and inferior mGCIPL sectors, should be incorporated to refine individual risk stratification and identify eyes with preserved structural potential for functional recovery. The lack of additional predictive value from RNFL parameters after adjustment underscores the limited utility of peripapillary thickness as an independent biomarker in this context. This study is subject to limitations. Retrospective ascertainment precludes full control of confounding, although uniform inclusion criteria and predefined analytic strategies were implemented. The sample size was modest, limiting power and precision, yet the estimates were directionally stable and aligned with existing evidence. Variable availability of examinations led to model-specific sample sizes; this was explicitly reported and addressed with hierarchical regression. Inclusion of both eyes maximises information but may leave residual inter-eye dependence in asymmetric presentations despite random-effects modelling. Recovery was assessed between 6 and 24 months, a pragmatic interval that may nonetheless introduce temporal heterogeneity. Future investigations should employ prospective, multicentre designs with standardised imaging protocols and fixed follow-up intervals, and leverage longitudinal modelling that combines sectoral macular OCT with microvascular (OCT-angiography) and functional/electrophysiological measures to yield externally validated prognostic algorithms. CONCLUSION Baseline MD remains the dominant predictor of postoperative visual outcome following chiasmal decompression. However, preservation of the nasal-superior, superior, and inferior macular GCIPL sectors confers additional, independent prognostic value, whereas RNFL parameters do not. Incorporation of sectoral macular OCT into the preoperative assessment may therefore enhance prognostic precision, optimize timing of intervention, and improve the quality of patient counseling and surgical decision-making. Declarations FUNDING DECLARATION The authors declare that no funding was received for this study. Clinical trial number: not applicable. Human Ethics and Consent to Participate declarations: not applicable. Author Contribution MV conceived and coordinated the study and drafted the manuscript.JV contributed to data acquisition and analysis.SSD performed the statistical analysis.AS, DD, FT, IS, JS, PM, and LN contributed to data collection and critical revision of the manuscript.JTF provided senior supervision and critical revision.All authors approved the final version of the manuscript. References Aflorei ED, Korbonits M. Epidemiology and etiopathogenesis of pituitary adenomas. J Neurooncol. 2014;117(3):379-394. doi:10.1007/s11060-013-1354-5 Scheithauer BW, Gaffey TA, Lloyd R V., et al. Pathobiology of Pituitary Adenomas and Carcinomas. Neurosurgery. 2006;59(2):341-353. doi:10.1227/01.NEU.0000223437.51435.6E Ezzat S, Asa SL, Couldwell WT, et al. The prevalence of pituitary adenomas. Cancer. 2004;101(3):613-619. doi:10.1002/cncr.20412 Fontana E, Gaillard R. [Epidemiology of pituitary adenoma: results of the first Swiss study]. Rev Med Suisse. 2009;5(223):2172-2174. Kidd D. The optic chiasm. Clinical Anatomy. 2014;27(8):1149-1158. doi:10.1002/ca.22385 Levy A. Pituitary disease: presentation, diagnosis, and management. J Neurol Neurosurg Psychiatry. 2004;75(suppl_3):iii47-iii52. doi:10.1136/jnnp.2004.045740 Cohen AR, Cooper PR, Kupersmith MJ, Flamm ES, Ransohoff J. Visual Recovery after Transsphenoidal Removal of Pituitary Adenomas. Neurosurgery. 1985;17(3):446-452. doi:10.1227/00006123-198509000-00008 Kerrison JB, Lynn MJ, Baer CA, Newman SA, Biousse V, Newman NJ. Stages of improvement in visual fields after pituitary tumor resection. Am J Ophthalmol. 2000;130(6):813-820. doi:10.1016/S0002-9394(00)00539-0 Tabaee A, Anand VK, Barrón Y, et al. Endoscopic pituitary surgery: a systematic review and meta-analysis. J Neurosurg. 2009;111(3):545-554. doi:10.3171/2007.12.17635 Jacob M, Raverot G, Jouanneau E, et al. Predicting Visual Outcome After Treatment of Pituitary Adenomas With Optical Coherence Tomography. Am J Ophthalmol. 2009;147(1):64-70.e2. doi:10.1016/j.ajo.2008.07.016 Danesh-Meyer H V., Papchenko T, Savino PJ, Law A, Evans J, Gamble GD. In Vivo Retinal Nerve Fiber Layer Thickness Measured by Optical Coherence Tomography Predicts Visual Recovery after Surgery for Parachiasmal Tumors. Investigative Opthalmology & Visual Science. 2008;49(5):1879. doi:10.1167/iovs.07-1127 Zhang J, Zhang S, Song Y, et al. Predictive value of preoperative retinal nerve fiber layer thickness for postoperative visual recovery in patients with chiasmal compression. Oncotarget. 2017;8(35):59148-59155. doi:10.18632/oncotarget.19324 Danesh-Meyer H V., Yoon JJ, Lawlor M, Savino PJ. Visual loss and recovery in chiasmal compression. Prog Retin Eye Res. 2019;73:100765. doi:10.1016/j.preteyeres.2019.06.001 Yum HR, Park SH, Park HYL, Shin SY. Macular Ganglion Cell Analysis Determined by Cirrus HD Optical Coherence Tomography for Early Detecting Chiasmal Compression. PLoS One. 2016;11(4):e0153064. doi:10.1371/journal.pone.0153064 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8418543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568339702,"identity":"0d559b95-9c34-485d-9ade-842b23b16870","order_by":0,"name":"Mariana 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Descobertas","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"","lastName":"Subtil","suffix":""},{"id":568339716,"identity":"fcd62c74-a7a3-4799-9a42-10c771f67fb4","order_by":8,"name":"Pedro Marques","email":"","orcid":"","institution":"Pituitary Tumor Unit, Endocrinology Department, Hospital CUF Descobertas","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Marques","suffix":""},{"id":568339717,"identity":"ca09ef07-255a-4bf5-8c59-64125ad4f3c4","order_by":9,"name":"Lia Neto","email":"","orcid":"","institution":"Neuro-Radiology Department, Unidade Local de Saúde Santa Maria","correspondingAuthor":false,"prefix":"","firstName":"Lia","middleName":"","lastName":"Neto","suffix":""},{"id":568339718,"identity":"fdab81f0-c0ab-4309-a653-78f1a8be7291","order_by":10,"name":"Joana Tavares Ferreira","email":"","orcid":"","institution":"Pituitary Tumor Unit, Neuro-Ophthalmology Department, Hospital CUF Descobertas","correspondingAuthor":false,"prefix":"","firstName":"Joana","middleName":"Tavares","lastName":"Ferreira","suffix":""}],"badges":[],"createdAt":"2025-12-21 16:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8418543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8418543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101880345,"identity":"07f7a814-3613-4664-9551-27e17b2f8e96","added_by":"auto","created_at":"2026-02-04 14:57:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":954802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8418543/v1/bea64785-41d5-4afc-9d1f-7e4c98f146c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eStructural and Functional Predictors of Visual Recovery in Chiasmal Compression Due to Sellar Tumors: Baseline MD, RNFL, and GCIPL\u003c/p\u003e","fulltext":[{"header":"KEY MESSAGE","content":"\u003cp\u003eWhat is known?\u003c/p\u003e\u003cp\u003e\u0026bull; Preoperative visual field impairment, particularly baseline mean deviation (MD), is a major determinant of postoperative visual recovery in patients with chiasmal compression due to sellar tumors.\u003c/p\u003e\u003cp\u003e\u0026bull; Optical coherence tomography (OCT)\u0026ndash;derived structural parameters, including peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell\u0026ndash;inner plexiform layer (GCIPL) thickness, are associated with functional outcomes in compressive optic neuropathies.\u003c/p\u003e\u003cp\u003eWhat is new?\u003c/p\u003e\u003cp\u003e\u0026bull; Specific macular GCIPL sectors (nasal-superior, superior, and inferior) demonstrate independent prognostic value for postoperative visual recovery beyond baseline MD.\u003c/p\u003e\u003cp\u003e\u0026bull; Peripapillary RNFL parameters do not provide additional independent prognostic information after adjustment for baseline functional deficit.\u003c/p\u003e\u003cp\u003e\u0026bull; Sectoral macular OCT analysis may enhance preoperative risk stratification and prognostic accuracy in patients undergoing chiasmal decompression.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eOptic neuropathy resulting from sellar and suprasellar masses, most commonly pituitary adenomas, represents a frequent and potentially reversible cause of visual dysfunction.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Pituitary tumors account for approximately 12\u0026ndash;15% of all intracranial neoplasms, and up to 70% of patients exhibit visual field deficits at the time of diagnosis.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The characteristic pattern of visual impairment, classically bitemporal hemianopia, arises from preferential damage to the decussating nasal retinal ganglion cell axons within the optic chiasm.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Surgical decompression of the chiasm often leads to partial or complete visual recovery; however, a significant proportion of patients experience incomplete restoration of function, despite technically successful interventions.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe ability to accurately predict visual outcomes following decompression is of substantial clinical importance. Reliable preoperative prognostic markers may aid in surgical planning, inform patient counseling, and assist in the optimization of treatment timing. Conventional predictors such as duration of symptoms, preoperative visual acuity or field loss, and tumor size have demonstrated limited prognostic consistency.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003eIn this context, structural neuro-ophthalmic imaging techniques have garnered attention for their potential to objectively assess the integrity of the anterior visual pathway.\u003c/p\u003e \u003cp\u003eOptical coherence tomography (OCT) has emerged as a valuable, non-invasive imaging modality for the evaluation of the optic nerve head and retinal architecture. Quantitative parameters derived from OCT, particularly the peripapillary retinal nerve fiber layer (RNFL) and the macular ganglion cell-inner plexiform layer (mGCIPL), have shown strong correlations with functional visual outcomes in compressive optic neuropathies. Several studies have demonstrated that preserved global RNFL thickness prior to surgical intervention is associated with more favorable postoperative visual recovery.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These findings reflect the value of RNFL thickness as a surrogate marker of axonal integrity and irreversible neuronal loss.\u003c/p\u003e \u003cp\u003eDespite the utility of global RNFL metrics, the prognostic relevance of regional RNFL and mGCIPL alterations remains underexplored. These regions are anatomically congruent with the fibers most susceptible to compressive injury at the level of the optic chiasm. Damage to the nasal quadrant, in particular, may more accurately reflect early or localized axonal compromise in the context of chiasmal compression. As such, a more granular analysis of quadrant-specific thickness could provide improved predictive value compared to global averages.\u003c/p\u003e \u003cp\u003eThe present study aims to investigate the predictive capacity of preoperative RNFL and mGCIPL thickness, as measured by OCT, for visual recovery following chiasmal decompression surgery. By focusing on these anatomically relevant sectors, this study seeks to identify objective, region-specific structural biomarkers that may enhance the accuracy of prognostic models, refine clinical decision-making, and ultimately contribute to improved patient outcomes in the management of compressive optic neuropathies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis retrospective observational study was conducted at a tertiary care center with multidisciplinary clinical support in neuroophthalmology, neurosurgery and endocrinology. The protocol adhered to the Declaration of Helsinki and received approval from the institutional ethics committee. The medical records of patients with optic neuropathy due to pituitary tumors treated until January 2025 were reviewed.\u003c/p\u003e \u003cp\u003eEligible participants comprised adults (\u0026ge;\u0026thinsp;18 years) who had undergone surgical resection of a pituitary tumor and for whom pre- and postoperative ophthalmic evaluations were available, including standard automated perimetry and spectral-domain optical coherence tomography (SD-OCT). Postoperative assessments were conducted within 6\u0026ndash;24 months following surgery.\u003c/p\u003e \u003cp\u003ePatients were excluded if they had concomitant eye diseases that could affect OCT or visual field results, such as glaucoma, diabetic retinopathy or optic neuritis, or if they had undergone unrelated neurosurgical procedures. Visual field tests were considered unreliable and excluded if they contained more than 25% fixation loss, false-positive or false-negative results. OCT scans were excluded if the signal quality was insufficient or the segmentation was inaccurate.\u003c/p\u003e \u003cp\u003eFunctional assessment consisted of standard automated perimetry using the Haag-Streit Octopus 900 perimeter, with mean deviation (MD) used to quantify visual field performance. Structural assessment was performed using the Heidelberg Spectralis\u0026reg; SD-OCT system. Measurements of peripapillary retinal nerve fiber layer (RNFL) thickness and macular ganglion cell inner plexiform layer (mGCIPL) thickness were analyzed.\u003c/p\u003e \u003cp\u003eThe primary outcome was postoperative visual field status, quantified by mean deviation (MD).\u003c/p\u003e \u003cp\u003eStatistical analyses were performed in IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) and Stata (version XX; StataCorp, College Station, TX, USA). Descriptive statistics summarized demographic, clinical, structural, and functional variables. Group comparisons and simple associations were conducted using parametric or non-parametric methods, as appropriate. To evaluate predictors of postoperative mean deviation (MD), we conducted hierarchical random-effects regression models to account for within-patient correlation. Univariable models were first estimated for each covariate; multivariable models then incorporated baseline MD together with a single OCT parameter (RNFL or mGCIPL). All statistical analysis were done for a significance level of 0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe demographic and clinical characteristics of the cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Twenty-eight patients were included (17 females, 60.7%; 11 males, 39.3%), with a mean age of 60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years (range, 38\u0026ndash;78). Non-functioning adenomas predominated (50.0%), followed by somatotroph adenomas (21.4%) and gonadotroph adenomas (7.1%); the remaining 21.4% comprised other histopathological entities as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Radiological evidence of chiasmal compression was documented in 57.1% of cases.\u003c/p\u003e \u003cp\u003eFor the regression analyses, both eyes were incorporated whenever the corresponding examinations were available and analyzable. Hierarchical random-effects models were employed to account for intra-individual (two-eye) correlation. Because several patients lacked valid results for one or more examinations, the number of observations varied across models, reflecting differences in the availability of reliable perimetric and OCT data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics of the Sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u0026ndash;78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTumor type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Non-functioning adenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Somatotroph adenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Gonadotroph adenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Others*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMRI evidence of chiasmal compression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ndash; Absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or number (percentage). MRI\u0026thinsp;=\u0026thinsp;magnetic resonance imaging. \u0026ldquo;Others\u0026rdquo; tumor types include tuberculum sellae meningioma, T-cell lymphoma, hemangioma, neurocytoma, neuroendocrine tumor, and Cushing\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eIn univariable analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), age (β\u0026thinsp;=\u0026thinsp;0.062; 95% CI, -0.248 to 0.372; p\u0026thinsp;=\u0026thinsp;0.694) and MRI evidence of chiasmal compression (β\u0026thinsp;=\u0026thinsp;2.568; 95% CI, -1.630 to 6.760; p\u0026thinsp;=\u0026thinsp;0.230) were not associated with postoperative visual outcomes. By contrast, baseline mean deviation (MD) demonstrated a robust positive association with follow-up MD (β\u0026thinsp;=\u0026thinsp;0.410; 95% CI, 0.250\u0026ndash;0.570; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Structural OCT parameters were likewise associated with outcome in univariable models: global RNFL thickness (β = -0.238; 95% CI, -0.320 to -0.160; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and all RNFL sectors, as well as global and sectoral mGCIPL measures, were significant (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable hierarchical regression models for predictors of follow-up MD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (Coef.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.248 to 0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI compression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.630 to 6.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.410\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.250 to 0.570\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRNFL Global\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.238\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.320 to -0.160\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNFL Nasal-Superior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.140 to -0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRNFL Temporal-Superior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.130 to -0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRNFL Temporal-Inferior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.140 to -0.050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRNFL Nasal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.180\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.260 to -0.100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRNFL Temporal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.310 to -0.080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Global\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.376\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.480 to -0.270\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Nasal-Superior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.240\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.360 to -0.120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Nasal-Inferior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.300\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.450 to -0.160\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Temporal-Superior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.370 to -0.130\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Temporal-Inferior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.350 to -0.160\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Superior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.320\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.440 to -0.200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emGCIPL Inferior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.380\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.500 to -0.270\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCoefficients (β) are shown with 95% confidence intervals (CI) and p-values. Statistically significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are presented in bold. MD\u0026thinsp;=\u0026thinsp;mean deviation; RNFL\u0026thinsp;=\u0026thinsp;retinal nerve fiber layer; mGCIPL\u0026thinsp;=\u0026thinsp;macular ganglion cell-inner plexiform layer; MRI\u0026thinsp;=\u0026thinsp;magnetic resonance imaging.\u003c/p\u003e \u003cp\u003eIn multivariable analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), baseline MD remained an independent predictor in all models that incorporated RNFL parameters and in selected mGCIPL models (superior and inferior). RNFL parameters did not retain statistical significance after adjustment for baseline MD. In contrast, three macular sectors preserved independent prognostic value: nasal-superior mGCIPL (β = -0.110; 95% CI, -0.190 to -0.020; p\u0026thinsp;=\u0026thinsp;0.014), superior mGCIPL (β = -0.140; 95% CI, -0.260 to -0.020; p\u0026thinsp;=\u0026thinsp;0.027), and inferior mGCIPL (β = -0.380; 95% CI, -0.500 to -0.270; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The remaining macular sectors and global mGCIPL did not retain significance after adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable hierarchical regression models including baseline MD and OCT parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel / Predictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (Coef.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Global: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.310\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.120 to 0.500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Global: RNFL Global\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.210 to 0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Nasal-Superior: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.420\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.230 to 0.600\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Nasal-Superior: RNFLNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.050 to 0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Nasal-Inferior: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.360\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.180 to 0.540\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Nasal-Inferior: RNFLNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.110 to 0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Temporal-Superior: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.370\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.210 to 0.520\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Temporal-Superior: RNFLTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.090 to 0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Temporal-Inferior: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.380\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.210 to 0.560\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Temporal-Inferior: RNFLTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.070 to 0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Nasal: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.340\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.150 to 0.540\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Nasal: RNFLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.160 to 0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Temporal: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.370\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.210 to 0.520\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;RNFL Temporal: RNFLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.190 to 0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Global: MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.090 to 0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Global: CCGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.310 to 0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Nasal-Superior: MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.070 to 0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Nasal-Superior: CCGNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.190 to -0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Nasal-Inferior: MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.090 to 0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Nasal-Inferior: CCGNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.240 to 0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Temporal-Superior: MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.070 to 0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Temporal-Superior: CCGTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.270 to 0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Temporal-Inferior: MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.090 to 0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Temporal-Inferior: CCGTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.240 to 0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Superior: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.350\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.150 to 0.540\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Superior: CCGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.260 to -0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Inferior: MD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.370\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.210 to 0.560\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMD\u0026thinsp;+\u0026thinsp;mGCIPL Inferior: CCGI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.380\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.500 to -0.270\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach model includes baseline MD and one structural parameter. Coefficients (β), 95% confidence intervals (CI), and p-values are reported. Statistically significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are presented in bold. MD\u0026thinsp;=\u0026thinsp;mean deviation; RNFL\u0026thinsp;=\u0026thinsp;retinal nerve fiber layer; mGCIPL\u0026thinsp;=\u0026thinsp;macular ganglion cell-inner plexiform layer; OCT\u0026thinsp;=\u0026thinsp;optical coherence tomography.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this cohort of patients with pituitary-region tumors undergoing neurosurgical chiasmal decompression, baseline visual field status, quantified by MD, emerged as the principal determinant of postoperative visual outcome, whereas sectoral macular structure provided additional, independent prognostic information. After adjustment for baseline MD, three mGCIPL sectors, nasal-superior, superior, and inferior, retained statistically significant associations with postoperative MD, while peripapillary RNFL parameters did not. These findings suggest that macular indices of ganglion-cell integrity capture residual neuronal reserve not entirely reflected by functional measures, whereas RNFL thickness conveys overlapping and, therefore, less discriminative prognostic information once baseline MD is considered.\u003c/p\u003e \u003cp\u003eThe sectoral distribution of these associations is anatomically and pathophysiologically consistent. The nasal-superior macular region contains ganglion-cell axons that decussate at the optic chiasm and are therefore particularly vulnerable to compressive lesions.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Conversely, the superior and inferior macular sectors correspond to fibers subserving the inferior and superior visual fields, respectively, regions of critical importance for central and paracentral vision. Preservation of these sectors likely reflects structural integrity of the retinal ganglion-cell populations most relevant for visual function and recovery potential. The persistence of these sectors as independent predictors supports the concept that sectoral macular OCT provides a more refined assessment of clinically meaningful structural damage than global macular or peripapillary metrics.\u003c/p\u003e \u003cp\u003ePrevious literature has underscored the prognostic relevance of OCT-derived measures in chiasmal compression. Danesh-Meyer et al.\u003csup\u003e13\u003c/sup\u003e demonstrated that greater preoperative peripapillary RNFL thickness is associated with superior postoperative visual recovery, establishing a structural-functional link in this setting. Subsequent studies reported that macular ganglion-cell metrics are particularly informative: macular GCIPL analysis facilitates early detection of chiasmal damage and correlates with postoperative function, with several cohorts highlighting sectoral associations in the nasal and superior regions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The present findings are concordant with this body of evidence, indicating that sectoral mGCIPL thickness, notably the nasal-superior, superior, and inferior sectors, provides prognostically relevant information beyond baseline perimetric status, whereas RNFL measures do not retain independent significance after adjustment.\u003c/p\u003e \u003cp\u003eFrom a clinical standpoint, these results support a hierarchical framework for prognostication in compressive optic neuropathy secondary to pituitary adenoma. Baseline MD should remain the reference parameter for estimating postoperative outcome, while sectoral macular OCT, especially the nasal-superior, superior, and inferior mGCIPL sectors, should be incorporated to refine individual risk stratification and identify eyes with preserved structural potential for functional recovery. The lack of additional predictive value from RNFL parameters after adjustment underscores the limited utility of peripapillary thickness as an independent biomarker in this context.\u003c/p\u003e \u003cp\u003eThis study is subject to limitations. Retrospective ascertainment precludes full control of confounding, although uniform inclusion criteria and predefined analytic strategies were implemented. The sample size was modest, limiting power and precision, yet the estimates were directionally stable and aligned with existing evidence. Variable availability of examinations led to model-specific sample sizes; this was explicitly reported and addressed with hierarchical regression. Inclusion of both eyes maximises information but may leave residual inter-eye dependence in asymmetric presentations despite random-effects modelling. Recovery was assessed between 6 and 24 months, a pragmatic interval that may nonetheless introduce temporal heterogeneity.\u003c/p\u003e \u003cp\u003eFuture investigations should employ prospective, multicentre designs with standardised imaging protocols and fixed follow-up intervals, and leverage longitudinal modelling that combines sectoral macular OCT with microvascular (OCT-angiography) and functional/electrophysiological measures to yield externally validated prognostic algorithms.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eBaseline MD remains the dominant predictor of postoperative visual outcome following chiasmal decompression. However, preservation of the nasal-superior, superior, and inferior macular GCIPL sectors confers additional, independent prognostic value, whereas RNFL parameters do not. Incorporation of sectoral macular OCT into the preoperative assessment may therefore enhance prognostic precision, optimize timing of intervention, and improve the quality of patient counseling and surgical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMV conceived and coordinated the study and drafted the manuscript.JV contributed to data acquisition and analysis.SSD performed the statistical analysis.AS, DD, FT, IS, JS, PM, and LN contributed to data collection and critical revision of the manuscript.JTF provided senior supervision and critical revision.All authors approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAflorei ED, Korbonits M. Epidemiology and etiopathogenesis of pituitary adenomas. J Neurooncol. 2014;117(3):379-394. doi:10.1007/s11060-013-1354-5\u003c/li\u003e\n\u003cli\u003eScheithauer BW, Gaffey TA, Lloyd R V., et al. Pathobiology of Pituitary Adenomas and Carcinomas. Neurosurgery. 2006;59(2):341-353. doi:10.1227/01.NEU.0000223437.51435.6E\u003c/li\u003e\n\u003cli\u003eEzzat S, Asa SL, Couldwell WT, et al. The prevalence of pituitary adenomas. Cancer. 2004;101(3):613-619. doi:10.1002/cncr.20412\u003c/li\u003e\n\u003cli\u003eFontana E, Gaillard R. [Epidemiology of pituitary adenoma: results of the first Swiss study]. Rev Med Suisse. 2009;5(223):2172-2174.\u003c/li\u003e\n\u003cli\u003eKidd D. The optic chiasm. Clinical Anatomy. 2014;27(8):1149-1158. doi:10.1002/ca.22385\u003c/li\u003e\n\u003cli\u003eLevy A. Pituitary disease: presentation, diagnosis, and management. J Neurol Neurosurg Psychiatry. 2004;75(suppl_3):iii47-iii52. doi:10.1136/jnnp.2004.045740\u003c/li\u003e\n\u003cli\u003eCohen AR, Cooper PR, Kupersmith MJ, Flamm ES, Ransohoff J. Visual Recovery after Transsphenoidal Removal of Pituitary Adenomas. Neurosurgery. 1985;17(3):446-452. doi:10.1227/00006123-198509000-00008\u003c/li\u003e\n\u003cli\u003eKerrison JB, Lynn MJ, Baer CA, Newman SA, Biousse V, Newman NJ. Stages of improvement in visual fields after pituitary tumor resection. Am J Ophthalmol. 2000;130(6):813-820. doi:10.1016/S0002-9394(00)00539-0\u003c/li\u003e\n\u003cli\u003eTabaee A, Anand VK, Barr\u0026oacute;n Y, et al. Endoscopic pituitary surgery: a systematic review and meta-analysis. J Neurosurg. 2009;111(3):545-554. doi:10.3171/2007.12.17635\u003c/li\u003e\n\u003cli\u003eJacob M, Raverot G, Jouanneau E, et al. Predicting Visual Outcome After Treatment of Pituitary Adenomas With Optical Coherence Tomography. Am J Ophthalmol. 2009;147(1):64-70.e2. doi:10.1016/j.ajo.2008.07.016\u003c/li\u003e\n\u003cli\u003eDanesh-Meyer H V., Papchenko T, Savino PJ, Law A, Evans J, Gamble GD. In Vivo Retinal Nerve Fiber Layer Thickness Measured by Optical Coherence Tomography Predicts Visual Recovery after Surgery for Parachiasmal Tumors. Investigative Opthalmology \u0026amp; Visual Science. 2008;49(5):1879. doi:10.1167/iovs.07-1127\u003c/li\u003e\n\u003cli\u003eZhang J, Zhang S, Song Y, et al. Predictive value of preoperative retinal nerve fiber layer thickness for postoperative visual recovery in patients with chiasmal compression. Oncotarget. 2017;8(35):59148-59155. doi:10.18632/oncotarget.19324\u003c/li\u003e\n\u003cli\u003eDanesh-Meyer H V., Yoon JJ, Lawlor M, Savino PJ. Visual loss and recovery in chiasmal compression. Prog Retin Eye Res. 2019;73:100765. doi:10.1016/j.preteyeres.2019.06.001\u003c/li\u003e\n\u003cli\u003eYum HR, Park SH, Park HYL, Shin SY. Macular Ganglion Cell Analysis Determined by Cirrus HD Optical Coherence Tomography for Early Detecting Chiasmal Compression. PLoS One. 2016;11(4):e0153064. doi:10.1371/journal.pone.0153064\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8418543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8418543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eINTRODUCTION\u003c/p\u003e\n\u003cp\u003eOptic neuropathy from sellar and suprasellar tumors is a frequent and potentially reversible cause of visual dysfunction. Predicting postoperative recovery is clinically relevant for surgical planning and patient counseling. The prognostic significance of regional OCT alterations remains unestablished. This study aimed to evaluate whether OCT quadrants predict postoperative visual recovery, and to determine which structural biomarkers independently contribute to prognosis.\u003c/p\u003e\n\u003cp\u003eMETHODS\u003c/p\u003e\n\u003cp\u003eThis retrospective study included patients with optic neuropathy secondary to sellar or suprasellar lesions surgically treated up to January 2025. Pre- and postoperative ophthalmologic evaluation included automated perimetry and spectral-domain OCT, and follow-up between 6-24 months. OCT parameters included global and sectoral RNFL and mGCIPL thickness. Random-effects hierarchical regression models were applied to account for within-patient correlation. Univariable models were performed for each covariate, followed by multivariable models incorporating baseline MD and one OCT parameter. Statistical analyses were performed using a significance level of 0.05.\u003c/p\u003e\n\u003cp\u003eRESULTS\u003c/p\u003e\n\u003cp\u003eTwenty-eight patients were included. In univariable analyses, neither age nor radiological evidence of chiasmal compression were significant predictors of postoperative visual outcome. Baseline MD showed a strong association with postoperative outcome (p \u0026lt; 0.001). All RNFL and mGCIPL sectors correlated significantly with follow-up MD (all p \u0026lt; 0.001). In multivariable models, baseline MD retained significance when combined with RNFL parameters, but lost significance when combined with macular GCIPL measures. RNFL parameters did not retain significance after adjustment. In contrast, three mGCIPL sectors retained independent prognostic value: nasal-superior (p = 0.014), superior (p = 0.027), and inferior (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eDISCUSSION AND CONCLUSION\u003c/p\u003e\n\u003cp\u003eBaseline MD was the most robust determinant of visual recovery after chiasmal decompression. Three GCIPL sectors, nasal-superior, superior, and inferior, emerged as independent predictors of postoperative outcome. Nasal-superior thickness highlights the vulnerability of decussating fibers at the chiasm. RNFL parameters did not add value beyond baseline MD, reflecting redundancy and possible floor effects. Clinically, sectoral macular OCT analysis should complement functional testing in preoperative evaluation.\u003c/p\u003e","manuscriptTitle":"Structural and Functional Predictors of Visual Recovery in Chiasmal Compression Due to Sellar Tumors: Baseline MD, RNFL, and GCIPL","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 09:28:26","doi":"10.21203/rs.3.rs-8418543/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75252ca8-b030-4f06-bed1-09e2b2c1ae45","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T09:28:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 09:28:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8418543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8418543","identity":"rs-8418543","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}