Dataset Information

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

ABSTRACT:

Purpose

To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.

Methods

Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experiments investigated the contribution of imaging features to the performance of the algorithms. Three FAF image regions were defined relative to GA: Lesion, Rim, and Background. For No Lesion, No Rim, and No Background datasets, a single region of interest was removed at a time. For Lesion, Rim, and Background Shuffled datasets, individual region pixels were randomly shuffled. For Lesion, Rim, and Background Mask datasets, masks of the regions were used. A Convex Hull dataset was generated to evaluate the importance of lesion size. Squared Pearson correlation (r2) was used to compare the predictive performance of ablated datasets relative to the benchmark.

Results

The Rim region influenced r2 more than the other two regions in all experiments, indicating the most relevant contribution of this region to the performance of the algorithms. In addition, similar performance was observed for all regions when pixels were shuffled or only a mask was used, indicating intensity information was not independently informative without textural context.

Conclusions

These ablation experiments enabled topographic clinical insights on FAF images from a DL-based GA progression prediction algorithm.

Translational relevance

Results from this study may lead to new insights on GA progression prediction.

SUBMITTER: Cluceru J

PROVIDER: S-EPMC11309039 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

Cluceru Julia J Anegondi Neha N Gao Simon S SS Lee Aaron Y AY Lad Eleonora M EM Chakravarthy Usha U Yang Qi Q Steffen Verena V Friesenhahn Michel M Rabe Christina C Ferrara Daniela D

Translational vision science & technology 20240801 8

<h4>Purpose</h4>To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.<h4>Methods</h4>Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experim ...[more]

PMID: 39102242

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Project description:ObjectiveTo identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care.DesignClinical evaluation of a deep learning-based algorithm.SubjectsOne hundred eighty-four eyes of 100 consecutively enrolled patients.MethodsOCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed.Main outcome measuresDice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth.ResultsThe deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80-0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively.ConclusionsThe proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Project description:ImportanceThe identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans.ObjectiveTo develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan.Design, setting, and participantsThis retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023.ExposureA position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3).Main outcomes and measuresPrediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.ResultsThe study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3).Conclusions and relevanceThe fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.

Project description:IntroductionTo evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification.MethodsA post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye.ResultsAnnual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (- 0.735 vs. - 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21).ConclusionsPegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF.Trial registrationClinical Trials identifier: NCT02503332.

Dataset Information

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

Purpose

Methods

Results

Conclusions

Translational relevance

Publications

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

Similar Datasets

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