Spectral domain OCT features in type 2 macular telangiectasia (type 2 MacTel): its relevance with clinical staging and visual acuity

Vitreomacular interface abnormalities in type 2 macular telangiectasia (MacTel)

PURPOSE

To describe the different types of vitreomacular interface abnormalities (VMIA) seen on optical coherence tomography (OCT) in type 2 macular telangiectasia (MacTel) and explain the possible reasons for its development.

METHODS

In this retrospective cross-sectional study, type 2 MacTel eyes with macular volumetric OCT imaging protocol were included to identify different types of VMIA such as abnormal PVD, vitreomacular traction (VMT), ERM, and lamellar and full-thickness macular hole. The VMIA findings were then correlated with different MacTel disease stages and visual acuity.

RESULTS

One thousand forty-three OCTs of 332 type 2 MacTel eyes from 169 patients at different visits were examined. VMIA was detected in 709 (68%) of those OCT scans in 216 (65%) eyes. There were 273 (39%), 31 (4%), 89 (13%), 7 (1%), and 381 (54%) OCT scans with vitreomacular adhesion, VMT, ERM, and inner and outer lamellar macular holes discovered respectively. VMIA eyes had a high frequency of abnormal PVD (p = 0.001) and retinal pigment clumps (RPCs) [p = 0.032]. Eyes with abnormal PVD (p = 0.034) and RPC (p = 0.000) had a higher rate of ERM development. RPC was linked to an increased risk of developing ERM (odd ratio 2.472; 95% CI 1.488-4.052). RPC and ERM contributed significantly to poor visual acuity (0.661 ± 0.416, 20/92).

CONCLUSION

OCT reveals a high frequency of VMIA in advanced type 2 MacTel eyes. RPC could be responsible for the development of anomalous PVD, as well as subsequent VMIAs and ERM. Additional work is required to examine the long-term changes and surgical outcomes of these eyes.

Quantification of vascular changes in macular telangiectasia type 2 with AngioTool software

PURPOSE

To compare AngioTool (AT) vascular parameters (VP) between MacTel2 eyes and normal eyes. Secondary outcome measures were to correlate VP with BCVA and to analyze VP between various grades of Simple MacTel Classification.

METHODS

This is a retrospective study. SD OCTA images of the superficial vascular complex (SVC) and deep capillary complex (DVC) were exported into Image J and AT. The explant area (EA), vessel area (VA), vessel percentage area (VPA), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL), total number of endpoints (TNE) and mean E lacunarity (MEL) were studied.

RESULTS

Group 1 had 120 MacTel2 eyes. Group 2 had 60 age-matched normal eyes. All VP were significantly different between the two groups except EA and TNE in both complexes. None of the VP had a correlation with BCVA. Interquadrant analysis (IQA) in SVC and DVC showed statistical significance in VPA, AVL and JD and in AVL, TNE, JD, VPA respectively. Post hoc analysis in SVC and DVC showed statistical significance in TNJ, JD, TVL and AVL between grade 1 and grade 3, and in VA, VPA, TNJ, JD, TVL and MEL between grade 0 and grade 3 respectively.

CONCLUSION

VP were affected in MacTel2 eyes. VP did not correlate with BCVA. Occurrence of pigmentation is an important event in the progression of disease. AT may provide quantitative markers to measure disease progression.

Right-angled vessel characteristics in different stages of type 2 macular telangiectasia (MacTel)

PURPOSE

To study right-angled vessels (RAV) in disease progression and macular neovascularization in type 2 macular telangiectasia (MacTel) eyes.

METHODS

This retrospective image analysis study examined type 2 MacTel patients' multicolour® and OCT imaging records from January 2015 to March 2023. Age, gender, laterality, visual acuity, systemic disease, and follow-up duration were recorded. RAV characteristics were assessed using OCT and multicolour® images. This study examined RAV characteristics and type 2 MacTel disease stage.

RESULTS

In total, 270 eyes of 146 patients (97 females, 66%) with a mean age of 60.77 ± 9.34 years were studied. 153 (57%) eyes showed RAV. The non-proliferative stage of type 2 MacTel had either no RAV or a normal-calibre right-angled vein, while the proliferative stage had a right-angled artery and a dilated or normal-calibre RAV [p < 0.001]. RAV characteristics differed at the final follow-up (p < 0.001). 11 eyes transitioned from non-proliferative to proliferative after a median period of 26 months (range: 5-96 months). RAV characteristics changed from a normal calibre right-angled vein at presentation to a normal calibre vein and artery in 6 (55%) eyes and to a dilated vein and artery in 5 (45%) eyes respectively.

CONCLUSION

RAV characteristics may indicate type 2 MacTel stages. A right-angled artery in type 2 MacTel may indicate proliferative disease.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2

Importance

Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.

Objective

To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data.

Design, Setting, and Participants

This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023.

Exposures

Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method).

Main Outcomes and Measures

The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods.

Results

A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders.

Conclusions and Relevance

The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review

Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various medical diseases, such as macular degeneration, glaucoma, and coronary artery disease. Despite its wide range of applications, the segmentation of OCT images remains difficult due to the complexity of tissue structures and the presence of artifacts. In recent years, different approaches have been used for OCT image segmentation, such as intensity-based, region-based, and deep learning-based methods. This paper reviews the major advances in state-of-the-art OCT image segmentation techniques. It provides an overview of the advantages and limitations of each method and presents the most relevant research works related to OCT image segmentation. It also provides an overview of existing datasets and discusses potential clinical applications. Additionally, this review gives an in-depth analysis of machine learning and deep learning approaches for OCT image segmentation. It outlines challenges and opportunities for further research in this field.

Baseline demographic, clinical and multimodal imaging features of young patients with type 2 macular telangiectasia

PURPOSE

Macular telangiectasia (MacTel) type 2 is observed in patients in their 5th-8th decades of life. The clinical and imaging findings in younger patients is unknown in larger cohorts. The study purpose is to report prevalence, baseline clinical and spectral domain optical coherence tomography (SDOCT) findings in young MacTel patients below 40 years.

METHODS

This hospital-based, multicentre, retrospective, cross-sectional study included patients between 2011 and 2023. Retinal photographs from multiple imaging techniques were evaluated to diagnose and stage type 2 MacTel and describe their SDOCT findings. Imaging characteristics were correlated with clinical stages and visual acuity.

RESULTS

Among all MacTel patients seen in hospital, prevalence of young MacTel cases less than age 40 was 1.77% (32/1806; 62 eyes). Youngest participant was 34 years, while mean age was 38.44 ± 1.795 years. Sixteen patients (50%) were diabetics. Perifoveal greying (n = 56, 90%) and perifoveal hyperreflective middle retinal layers (n = 47, 76%) were the most prevalent clinical and SDOCT imaging finding respectively. Less than 10% (n = 6) eyes had proliferative disease. Presence of retinal pigment clumps (RPC) (7% vs. 67%; p = 0.002) coincided with proliferative MacTel. Poor vision was associated with presence of outer retinal layer SDOCT findings like outward bending of inner retinal layers (p = 0.047), RPC (p = 0.007), subfoveal neurosensory detachment (p = 0.048) and subretinal neovascular membrane (p = 0.001).

CONCLUSION

Type 2 MacTel before age 40 is rare, common in women and diabetics, and affects vision in advanced stage. Disease symmetry, comparison with older cases, and longitudinal SDOCT changes in such patients require further study.

Early Optical Coherence Tomography Biomarkers for Selected Retinal Diseases-A Review

Optical coherence tomography (OCT) is a non-invasive, easily accessible imaging technique that enables diagnosing several retinal diseases at various stages of development. This review discusses early OCT findings as non-invasive imaging biomarkers for predicting the future development of selected retinal diseases, with emphasis on age-related macular degeneration, macular telangiectasia, and drug-induced maculopathies. Practitioners, by being able to predict the development of many conditions and start treatment at the earliest stage, may thus achieve better treatment outcomes.

OCT and OCT Angiography Update: Clinical Application to Age-Related Macular Degeneration, Central Serous Chorioretinopathy, Macular Telangiectasia, and Diabetic Retinopathy

Similar to ultrasound adapting soundwaves to depict the inner structures and tissues, optical coherence tomography (OCT) utilizes low coherence light waves to assess characteristics in the eye. Compared to the previous gold standard diagnostic imaging fluorescein angiography, OCT is a noninvasive imaging modality that generates images of ocular tissues at a rapid speed. Two commonly used iterations of OCT include spectral-domain (SD) and swept-source (SS). Each comes with different wavelengths and tissue penetration capacities. OCT angiography (OCTA) is a functional extension of the OCT. It generates a large number of pixels to capture the tissue and underlying blood flow. This allows OCTA to measure ischemia and demarcation of the vasculature in a wide range of conditions. This review focused on the study of four commonly encountered diseases involving the retina including age-related macular degeneration (AMD), diabetic retinopathy (DR), central serous chorioretinopathy (CSC), and macular telangiectasia (MacTel). Modern imaging techniques including SD-OCT, TD-OCT, SS-OCT, and OCTA assist with understanding the disease pathogenesis and natural history of disease progression, in addition to routine diagnosis and management in the clinical setting. Finally, this review compares each imaging technique's limitations and potential refinements.

Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10

Purpose

To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging.

Design

An algorithm was used on data from a prospective natural history study of MacTel for classification development.

Subjects

A total of 1733 participants enrolled in an international natural history study of MacTel.

Methods

The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity.

Main Outcome Measures

The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes.

Results

The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed.

Conclusions

This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Is Type 2 Macular Telangiectasia a Bilateral and Symmetrical Disease Entity?

Purpose

To study the inter-eye asymmetry in cases diagnosed with type 2 macular telangiectasia (MacTel).

Methods

Herein, type 2 MacTel cases were staged as per Gass and Blodi classification with multiple imaging techniques. Based on disease stage symmetry, two groups identified. Group 1: Symmetrical stage and Group 2: Asymmetrical stage MacTel disease. Prevalence, demography, and clinical features of MacTel cases showing inter-eye asymmetry were analyzed.

Results

Two hundred and eighty eyes of 140 patients diagnosed clinically with type 2 MacTel (84-Group 1 and 56-Group 2) were evaluated. Eighty-nine (64%) were female, and the median age of the entire cohort was 62.5 years (inter-quartile range: 57.0-68.75). MacTel disease with asymmetric stage was seen in 56 (40%) of the 140 patients. At presentation, a two-stage difference was noted in 46% (n = 26) of the patients with asymmetrical MacTel disease. A 10% conversion from symmetrical to asymmetrical disease stage was noted at the final visit. Of the 280 eyes evaluated for type 2 MacTel disease, 12 (4%) eyes showed no findings suggestive of MacTel on clinical examination and fluorescein angiography, optical coherence tomography (OCT), and OCT angiography when available and were labeled as unilateral type 2 MacTel disease.

Conclusions

Type 2 MacTel can show inter-eye disease stage asymmetry. Unilateral type 2 MacTel disease is a distinct stage in MacTel which would need further evaluation and consideration while staging.