Optical coherence tomography predictors of progression of non-exudative age-related macular degeneration to advanced atrophic and exudative disease

Amarasekera, Sohani; Samanta, Anindya; Jhingan, Mahima; Arora, Supriya; Singh, Sumit Randhir; Tucci, Davide; Lupidi, Marco; Chhablani, Jay

doi:10.1007/s00417-021-05419-2

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…5–8 Many OCT biomarkers for progression to iRORA or cRORA have been proposed, including disruption of outer retinal layers, intraretinal hyporeflective space, drusen collapse, high-central drusen volume (≥0.03 mm 3 ), intraretinal hyperreflective foci, subretinal drusenoid deposits (SDDs), hyporeflective drusen cores, thin double-layer sign (DLS), and cRORA in the fellow eye. 9,10…”

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confidence: 99%

“…[5][6][7][8] Many OCT biomarkers for progression to iRORA or cRORA have been proposed, including disruption of outer retinal layers, intraretinal hyporeflective space, drusen collapse, high-central drusen volume ($0.03 mm 3 ), intraretinal hyperreflective foci, subretinal drusenoid deposits (SDDs), hyporeflective drusen cores, thin double-layer sign (DLS), and cRORA in the fellow eye. 9,10 For measuring the areas of atrophy on OCT, the key feature seems to be the hypertransmission defect (hyperTD), which is most easily discerned in the choroid. In atrophic lesions, the hyperTD arises due to increased transmission of signal to the choroid due to loss of signal attenuation from the disrupted or absent overlying RPE and outer retina.…”

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confidence: 99%

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Prevalence and Persistence of Hypertransmission Defects of Various Sizes in Eyes with Intermediate Age-related Macular Degeneration

Nanegrungsunk,

Corradetti,

Phinyo

et al. 2023

Retina

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Purpose: To determine the prevalence and rate of persistence over two years of various-sized hypertransmission defects (hyperTDs) in eyes with intermediate age-related macular degeneration (iAMD). Methods: Retrospective analysis of optical coherence tomography (OCT) data from consecutive iAMD patients. Choroidal en face OCT images were evaluated for the presence and number of hyperTDs of three different sizes based on greatest linear dimension (small, 63-124 µm; medium, 125-249 µm; large, ≥250 µm) at baseline and at 2-years follow-up. Interreader agreement was determined by Gwet’s agreement coefficient (AC). Disagreements between graders were resolved by the senior investigator to yield a single consensus for all cases. Results: From 273 iAMD eyes (247 patients), 72 and 76 hyperTD lesions were independently identified by two graders at baseline and overall AC was 0.89 (95% CI, 0.86-0.93). After adjudication by the senior grader, the final consensus yielded 78 hyperTD lesions from 46 eyes (16.8%) of 42 patients (17.0%) in this study cohort. Among eyes with follow-up OCT, 32 of 45 hyperTD lesions (71.1%) persisted. The rates of persistence were 100.0%, 72.7% and 53.3% in large, medium and small hyperTD sizes, respectively. Conclusion: HyperTDs were present in a significant proportion of iAMD eyes. Acceptable interreader agreement was demonstrated in identifying hyperTD. Larger hyperTD lesions were more likely to persist over two years.

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confidence: 99%

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confidence: 99%

Prevalence and Persistence of Hypertransmission Defects of Various Sizes in Eyes with Intermediate Age-related Macular Degeneration

Nanegrungsunk,

Corradetti,

Phinyo

et al. 2023

Retina

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“…Therefore, there has been increasing interest in intervening at earlier stages of the disease. A number of studies have identified several high-risk biomarkers on structural OCT B-scan, such as intraretinal hyperreflective foci, subretinal drusenoid deposits, drusen with hyporeflective cores and high central drusen volume, which appear to be associated with a higher risk of progression from intermediate to late AMD [ 11 – 13 , 19 – 20 ]. Our group has previously investigated the utility of SLIVER-net in automated detection of these high-risk biomarkers in a small annotated OCT dataset with good performance, sometimes better than retina specialists [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers

Rudas

Chiang²,

Corradetti³

et al. 2023

PLOS Digit Health

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Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

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“…A number of studies have identified several high-risk biomarkers on structural OCT B-scan, such as intraretinal hyperreflective foci, subretinal drusenoid deposits, drusen with hyporeflective cores and high central drusen volume, which appear to be associated with a higher risk of progression from intermediate to late AMD. (Nassisi et al 2019) (Lei et al 2017; Nassisi et al 2018) (Corradetti et al 2021) (Nassisi et al 2019) (Amarasekera et al 2021) Our group has previously investigated the utility of SLIVER-net in automated detection of these high-risk biomarkers in a small annotated OCT dataset with good performance, sometimes better than retina specialists. (Rakocz et al 2021) In this study, we used these AMD progression biomarkers to predict the conversion to exudative AMD.…”

Section: Discussionmentioning

confidence: 99%

Automated large-scale AMD progression prediction using machine-read OCT biomarkers

Rudas

Chiang

Corradetti

et al. 2022

Preprint

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Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. While anti-vascular growth factor injections can be used to treat macular neovascularization (MNV), there are currently no treatments available to halt or reverse geographic atrophy, which is the late-stage of nonneovascular AMD. There is a great interest in detecting early biomarkers associated with a higher risk for AMD progression in order to design early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers, and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm significantly outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

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Optical coherence tomography predictors of progression of non-exudative age-related macular degeneration to advanced atrophic and exudative disease

Cited by 13 publications

References 16 publications

Prevalence and Persistence of Hypertransmission Defects of Various Sizes in Eyes with Intermediate Age-related Macular Degeneration

Prevalence and Persistence of Hypertransmission Defects of Various Sizes in Eyes with Intermediate Age-related Macular Degeneration

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers

Automated large-scale AMD progression prediction using machine-read OCT biomarkers

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