The Temporal Retinal Nerve Fiber Layer Thickness Is the Most Important Optical Coherence Tomography Estimate in Multiple Sclerosis

Birkeldh, Ulrika; Manouchehrinia, Ali; Hietala, Max Albert; Hillert, Jan; Olsson, Tomas; Piehl, Fredrik; Kockum, Ingrid; Brundin, Lou; Zahavi, Ori; Wahlberg-Ramsay, Marika; Brautaset, Rune; Nilsson, Maria

doi:10.3389/fneur.2017.00675

Cited by 45 publications

(41 citation statements)

References 39 publications

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“…These locations correspond to the location of small RGCs which are more susceptible to be damaged than larger ones [32]. Our results are totally in accordance with other works [33][34].…”

Section: Discussionsupporting

confidence: 92%

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

et al. 2019

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Objective To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease. Methods Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population. Results Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL. Conclusions Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.

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“…These locations correspond to the location of small RGCs which are more susceptible to be damaged than larger ones [32]. Our results are totally in accordance with other works [33][34].…”

Section: Discussionsupporting

confidence: 92%

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

et al. 2019

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“…First OCT studies focused on global RNFL thickness as a maker of axonal integrity in the retina, but current segmentation techniques have enabled detailed quantification of specific retinal segments [22]. The distribution of RNFL loss shows a predilection for the temporal quadrant [23]. In our analysis of non-ON MS patients eyes the significant optic disc RNFL thinning was observed specifically in the superior quadrant, which is consistent with earlier published report [24].…”

Section: Discussionsupporting

confidence: 88%

The Role of Optical Coherence Tomography in Differential Diagnosis of Multiple Sclerosis and Autoimmune Connective Tissue Diseases with CNS Involvement

Wildner

Zydorczak

Oset

et al. 2020

JCM

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The purpose of this study was to examine whether application of optical coherence tomography (OCT) measurements can provide a useful biomarker for distinguishing central nervous system (CNS) involvement in autoimmune connective tissue diseases (CTD) from multiple sclerosis (MS). An observational study included non-optic neuritis eyes of 121 individuals: 59 patients with MS, 30 patients with CNS involvement in CTD, and 32 healthy controls. OCT examination was performed in all subjects to measure retinal nerve fiber layer (RNFL) thickness, ganglion cell complex (GCC) thickness, ganglion cell layer-inner plexiform layer (GCIPL) thickness, and volume of the macula. There was a significant group effect with regard to superior optic disc RNFL, macular RNFL, GCC, and GCIPL thickness, and macular volume. Post-hoc analysis revealed that MS patients have significantly smaller macular volume and thinner superior optic disc RNFL, macular RNFL, GCC, and GCIPL compared to healthy controls. CTD patients have significantly smaller superior optic disc RNFL, GCIPL, and GCC thickness compared to healthy controls. However, no significant group differences were observed between the patient groups (MS vs. CTD) on any outcome. Although a prominent retinal thinning may be a useful biomarker in MS patients, in a general population of individuals with a confirmed CNS involvement the use of OCT is not specific enough to discriminate between MS and autoimmune CTD.

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“…A decrease in macular volume has been noted in patients with multiple sclerosis, more so in the nasal portions, which may corroborate with compromised retinal nerve fibre layer thickness in the temporal quadrant as reported in a previous study . Evangelou et al examined the size distribution of neurons in the magnocellular and parvocellular layers of the lateral geniculate body, and axonal densities of the anterior visual pathway.…”

Section: Multiple Sclerosissupporting

confidence: 65%

“…The magnitude of this damage has been reported to be 20–25 per cent when compared to the fellow unaffected eye . Retinal nerve fibre layer loss is usually generalised, affecting all quadrants, but has a predilection toward the temporal quadrant, suggesting that the papillomacular bundle is most affected.…”

Section: Multiple Sclerosismentioning

confidence: 99%

Optical coherence tomography in the investigation of systemic neurologic disease

Srinivasan

Efron

2019

Clinical and Experimental Optometry

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Optical coherence tomography (OCT) is a well-established technique for the clinical examination, diagnosis, severity staging and monitoring of ophthalmic disorders. The application of this technology has more recently been extended beyond ophthalmic disease, whereby it has been demonstrated that OCT can serve as an ophthalmic marker for a range of systemic neurodegenerative disorders, such as Parkinson's disease, multiple sclerosis, Alzheimer's disease and diabetic peripheral neuropathy. This review will focus on the clinical utility of OCT-derived retinal measures for the investigation of these conditions.

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The Temporal Retinal Nerve Fiber Layer Thickness Is the Most Important Optical Coherence Tomography Estimate in Multiple Sclerosis

Cited by 45 publications

References 39 publications

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

The Role of Optical Coherence Tomography in Differential Diagnosis of Multiple Sclerosis and Autoimmune Connective Tissue Diseases with CNS Involvement

Optical coherence tomography in the investigation of systemic neurologic disease

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