Dataset Information

Evaluation of optic nerve subarachnoid space in primary open angle glaucoma using ultrasound examination.

ABSTRACT:

Objectives

To measure Optic Nerve Subarachnoid Space (ONSAS) in patients with primary open-angle glaucoma (POAG) and controls using A-scan ultrasound and to evaluate the measurement of the ONSAS in relation to age patient and OCT parameters.

Methods

This retrospective study included 53 consecutive eyes of 27 patients with POAG and 64 normal eyes of 32 controls. Both glaucomatous and control groups were divided into 2 subgroups according to age: <60 age (glaucomatous and control group 1) and 61-90 age (glaucomatous and control group 2).

Results

The ONSAS was significantly lower in all glaucomatous eyes (3.54 ± 0.38) versus normal eyes (3.87 ± 0.32) (p = 0.001). Significant reduction of ONSAS was showed in control group 2 (3.63 mm ± 0.37) compared to control group 1 (3.87 mm ± 0.32) (p = 0.014) and between glaucoma group 1 (3.54 mm ± 0.38) and control group 1 (p = 0.001). While no significant differences were observed between glaucomatous group 2 (3.48 mm ± 0.41) and control group 2 (p = 0.17) and between glaucoma group 1 and glaucoma group 2 (p = 0.609). Lastly, the ONSAS was not significantly associated with GCC and RNFL parameters except for Focal Loss Volume (FLV), Superior RNFL and ONSAS in glaucoma group 1 and for FLV and ONSAS in all glaucomatous group.

Conclusion

Standardized A-scan ultrasound is a non invasive imaging technique with which it is possible to monitor ONSAS changes in glaucomatous patients. The reduction of ONSAS confirm the importance of the lower orbital CSFP as further risk factor in the progression of glaucoma disease.

SUBMITTER: Cennamo G

PROVIDER: S-EPMC6261615 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Evaluation of optic nerve subarachnoid space in primary open angle glaucoma using ultrasound examination.

Cennamo Gilda G Montorio Daniela D Breve Maria Angelica MA Brescia Morra Vincenzo V Menna Feliciana F Cennamo Giovanni G

PloS one 20181128 11

<h4>Objectives</h4>To measure Optic Nerve Subarachnoid Space (ONSAS) in patients with primary open-angle glaucoma (POAG) and controls using A-scan ultrasound and to evaluate the measurement of the ONSAS in relation to age patient and OCT parameters.<h4>Methods</h4>This retrospective study included 53 consecutive eyes of 27 patients with POAG and 64 normal eyes of 32 controls. Both glaucomatous and control groups were divided into 2 subgroups according to age: <60 age (glaucomatous and control gr ...[more]

PMID: 30485355

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Project description:PurposeGlaucoma is the primary cause of permanent vision loss worldwide. However, the pathogenesis of primary open-angle glaucoma (POAG), the main type of glaucoma, has not yet been completely understood.MethodsIn our study, the POAG cohorts were obtained from the Gene Expression Omnibus (GEO) database (GSE45570). Biomarkers with diagnostic utility for POAG were identified through combining differentially expressed analysis, enrichment analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. The regulatory networks (including a competing endogenous RNA (ceRNA) regulatory network and a small molecule compounds-mRNA network) were created. In addition, the Mendelian randomization (MR) analysis was used to identify exposures causally associated with POAG. Finally, the expression of the biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR).ResultsThe Gene Ontology (GO) items that the differentially expressed genes (DEGs) between POAG and control groups enriched were relevant to light stimulation and DNA methylation. A total of three light stimulation-related biomarkers (RAB8A, PRG3, and SMAD3) were identified, which had diagnostic value for POAG patients. Besides, the ceRNA regulatory network contained 88 nodes and 93 edges, and a small molecule compounds-mRNA network included 66 nodes and 76 edges. The MR results indicated a causal association between DNA methylation GrimAge acceleration and POAG. Additionally, the results of RT-qPCR revealed that the expression trend of RAB8A was consistent with that of GSE45570.ConclusionsTaken together, this study provides three light stimulation-related biomarkers (RAB8A, PRG3, and SMAD3) for the diagnosis of POAG, providing scientifically valuable insights for further studies of POAG.Translational relevanceDiscovering biomarkers that possess diagnostic significance for POAG has the potential to offer new insights into the pathogenesis of POAG and present novel objectives for clinical intervention.

Project description:PurposePrimary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. Although deep learning methods have been proposed to diagnose POAG, these methods all used a single image as input. Contrastingly, glaucoma specialists typically compare the follow-up image with the baseline image to diagnose incident glaucoma. To simulate this process, we proposed a Siamese neural network, POAGNet, to detect POAG from optic disc photographs.DesignThe POAGNet, an algorithm for glaucoma diagnosis, is developed using optic disc photographs.ParticipantsThe POAGNet was trained and evaluated on 2 data sets: (1) 37 339 optic disc photographs from 1636 Ocular Hypertension Treatment Study (OHTS) participants and (2) 3684 optic disc photographs from the Sequential fundus Images for Glaucoma (SIG) data set. Gold standard labels were obtained using reading center grades.MethodsWe proposed a Siamese network model, POAGNet, to simulate the clinical process of identifying POAG from optic disc photographs. The POAGNet consists of 2 side outputs for deep supervision and uses convolution to measure the similarity between 2 networks.Main outcome measuresThe main outcome measures are the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.ResultsIn POAG diagnosis, extensive experiments show that POAGNet performed better than the best state-of-the-art model on the OHTS test set (area under the curve [AUC] 0.9587 versus 0.8750). It also outperformed the baseline models on the SIG test set (AUC 0.7518 versus 0.6434). To assess the transferability of POAGNet, we also validated the impact of cross-data set variability on our model. The model trained on OHTS achieved an AUC of 0.7490 on SIG, comparable to the previous model trained on the same data set. When using the combination of SIG and OHTS for training, our model achieved superior AUC to the single-data model (AUC 0.8165 versus 0.7518). These demonstrate the relative generalizability of POAGNet.ConclusionsBy simulating the clinical grading process, POAGNet demonstrated high accuracy in POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. The POAGNet is publicly available on https://github.com/bionlplab/poagnet.

Dataset Information

Evaluation of optic nerve subarachnoid space in primary open angle glaucoma using ultrasound examination.

Objectives

Methods

Results

Conclusion

Publications

Evaluation of optic nerve subarachnoid space in primary open angle glaucoma using ultrasound examination.

Similar Datasets

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