Abstract
Purpose:
To develop a data-driven interpretable predictive model of incoming drusen regression as a sign of disease activity and identify optical coherence tomography (OCT) biomarkers associated with its risk in intermediate age-related macular degeneration (AMD).
Methods:
Patients with AMD were observed every 3 months, using Spectralis OCT imaging, for a minimum duration of 12 months and up to a period of 60 months. Segmentation of drusen and the overlying layers was obtained using a graph-theoretic method, and the hyperreflective foci were segmented using a voxel classification method. Automated image analysis steps were then applied to identify and characterize individual drusen at baseline, and their development was monitored at every follow-up visit. Finally, a machine learning method based on a sparse Cox proportional hazard regression was developed to estimate a risk score and predict the incoming regression of individual drusen.
Results:
The predictive model was trained and evaluated on a longitudinal dataset of 61 eyes from 38 patients using cross-validation. The mean follow-up time was 37.8 ± 13.8 months. A total of 944 drusen were identified at baseline, out of which 249 (26%) regressed during follow-up. The prediction performance was evaluated as area under the curve (AUC) for different time periods. Prediction within the first 2 years achieved an AUC of 0.75.
Conclusions:
The predictive model proposed in this study represents a promising step toward image-guided prediction of AMD progression. Machine learning is expected to accelerate and contribute to the development of new therapeutics that delay the progression of AMD.
Age-related macular degeneration (AMD) is still the leading cause of irreversible visual loss in the elderly population.
1–3 Over time the disease progresses relentlessly toward late AMD. Late AMD can be broken down into two general forms, atrophic or neovascular; however, interindividual disease progression is variable, and not all high-risk features in a macula progress to late AMD within an individual. The pathogenesis of AMD is still relatively unclear, and currently there is an effective treatment available only for the less common, neovascular form. The introduction of optical coherence tomography (OCT) has had a profound impact on the assessment, early detection, and monitoring of AMD progression by facilitating three-dimensional (3D) phenotyping of the retina and the neurosensory layers in fine detail. Thus, to expedite the search for therapies that could halt the progression of intermediate to late AMD, it is essential to be able to identify early pathomorphologic changes and predict individual AMD progression using adequate biomarkers that are accessible by OCT imaging.
A clinical hallmark of early AMD is the presence of drusen, which are focal deposits of cellular waste products that begin to accumulate between the retinal pigment epithelium (RPE) and Bruch's membrane (BM). Excess drusen deposition can lead to damage of the RPE and an inflammatory or degenerative reaction that can result in retinal atrophy, the expression of vascular endothelial growth factor (VEGF) and subsequent neovascularization, or both.
2 Drusen are dynamic structures that can increase in size, fuse, or regress.
4 A drusen-related event of clinical interest is drusen regression. It is a naturally occurring phenomenon whereby drusen spontaneously decrease in size or completely disappear. Although some eyes showed regression without subsequent late AMD onset, in many cases late AMD developed precisely at the location where drusen regressed
5–7; hence drusen regression is a potential surrogate anatomic endpoint of intermediate AMD.
8 However, how to effectively predict drusen regression and its associated predictive markers at an individual level is currently unclear, and there is an ongoing research effort with the aim to identify individuals and best timing for intervention. We hypothesize that using exhaustive quantitative characterization of drusen on OCT in combination with machine learning methods can reveal the risk of incoming regression, which has failed using conventional evaluation.
In this paper, we propose a data-driven predictive model of incoming drusen regression (
Fig. 1). It presents a substantial extension of our previous work on this topic.
9 For the learning, we utilized a longitudinal dataset from a prospective observational study, consisting of OCT images of 61 eyes with early/intermediate AMD, acquired at 3-month intervals. We developed an OCT-based drusen characterization using automated image analysis methods of the outer retina, with a focus on its shape, local appearance of its structure, and that of the overlying neurosensory layers, as well as its short-term longitudinal change. Using such characterization, we developed a machine learning method based on survival analysis to predict regression at the level of each individual druse. The predictive model is evaluated using leave-one-patient-out cross-validation, and the OCT biomarkers associated with the successful prediction are reported.
OCT Imaging Protocol.
Imaging was performed with Spectralis spectral-domain OCT (Heidelberg Engineering, Heidelberg, Germany), which acquires anisotropic 3D images having 1024 × 97 × 496 voxels with the size of 5.7 × 60.5 × 3.87 μm3, covering the volume of 6 × 6 × 2 mm3. In addition, confocal scanning laser ophthalmoscopy (SLO) was used to acquire an isotropic 2D fundus image of the same field of view, with superior spatial resolution of 1536 × 1536 pixels with the size of 5.7 × 5.7 μm2. The SLO fundus image and the OCT image are acquired with the same optics and are coregistered by the imaging device.
Outer Retinal Layer Segmentation.
Hyperreflective Foci (HRF) Segmentation.
Individual Drusen Segmentation.
Intrapatient Spatial Alignment.
Drusen Characterization.
Time Point of Drusen Regression.
Intermediate AMD progresses in remarkably varied ways across patients,
25 and there are currently no known sensitive and specific biomarkers indicating type and timing of individual AMD progression.
7 Detecting late AMD at the time of its onset is crucial for initiating effective therapy and preventing vision loss,
26 but as the onset of late AMD has often already resulted in irreversible vision loss, therapeutic interventions need to ultimately target AMD at an intermediate stage when function is still intact. Efficient screening in millions of patients with drusen can be undertaken only if the pathognomonic risk factors for progression/conversion are recognized and targeted. Furthermore, the availability of robust biomarkers for disease progression is a crucial prerequisite for the development of innovative therapeutic strategies, particularly in a slowly and variably progressing disease such as intermediate AMD. The pathways leading from intermediate to late AMD often have a preceding event of drusen regression in common.
4 In this work, we developed an interpretable predictive model of individual drusen regression in a data-driven way, in an effort to predict and identify markers of risk of imminent drusen regression.
We developed a machine learning–based method that uses a large set of biomarkers to estimate the risk of regression (HR score), at the level of an individual druse. We benefited from an exceptionally adequate study of patients with intermediate AMD, imaged on a regular 3-month basis. The model relies on imaging biomarkers measured at baseline and the first follow-up visit, only 3 months apart. The evaluation showed that the obtained model is of value for predicting drusen events within the following 2 years, having an AUC performance of 0.75. Observing the selected features of the sparse regression model revealed that the mean drusen thickness, maximum drusen height, and the attenuation had the greatest impact. An additional benefit of using sparse models is that we need only to segment and quantify the few features used by the model in order to make predictions, saving time on image processing and analysis.
In this work, we use HRF as a general term for locally hyperreflective structures with reflectivity in the order of the RPE or greater. They are assumed to be a combination of accumulated lipids, microglia, and migrating or transdifferentiating RPE cells.
27 We did not distinguish among different types of HRF conglomeration but differentiated them by the layer in which they reside, that is, whether directly on top of drusen in the ORB or further above in the ONL. HRF volume in ONL was found to be related to regression but not as strongly as drusen shape and attenuation-based features. The role of HRF in our work may be underestimated due to different HRF types being considered and pooled together; hence further HRF subtyping is part of our future work. Another difficulty in comparing HRF properties and its role with related work is that different authors might consider different objects as HRF due to their loose definition.
Understanding the phenomenon of drusen regression started with studies observing the natural history of AMD progression. The basic work of Sarks
28 was guiding the path toward understanding of drusen biology as it could clearly be shown that a stage of incipient atrophy can be recognized as an area of diffuse hyperfluorescence in which pigment clumping or reticular pigment figures and fading of drusen occur.
28 Yehoshua et al.
5 characterized drusen by total volume and area, but the regression could not be successfully predicted. Ouyang et al.
6 found the presence of HRF overlying drusen and the heterogeneous internal drusen reflectivity to be related with the local onset of atrophy in the ensuing months. Querques et al.
29 reported calcifications inside the regressing drusen.
Drusen properties have been previously inspected for their role in predicting conversion to late AMD. In de Sisternes et al.,
30 the area, volume, height, and reflectivity were found to be informative features for the transition to exudative AMD. Abdelfattah et al.
31 found that baseline drusen volume was a predictor of conversion to late AMD in eyes that already had neovascular AMD in the fellow eyes. Reflective drusen substructures were found to be predictive of progression to geographic atrophy.
32 In the work of Folgar et al.,
21 drusen volume and RPE abnormal thinning volume were found to be related with the risk of progression to late AMD. However, all of these studies offered recommendations based on properties summarized over all the drusen present.
An important distinction of our approach is that we obtained a personalized predictive model at the level of individual drusen, which enabled us to generate estimates of personalized future regression maps as shown in
Figure 9. In addition, to the best of our knowledge this is the first time quantitative properties of HRF were used and not just the status of their presence. Machine learning applied on longitudinal OCT imaging data has recently been shown to be a powerful approach for personalized predictive modeling in a growing number of ophthalmic applications, including predicting recurrence of macular edema.
33 anti-VEGF treatment responders,
34 progression to late AMD,
30 and progression of geographic atrophy.
35
Previous analysis of drusen volume development in this patient cohort
10 has been performed using polarization-sensitive (PS) OCT, which measures the polarization state of backscattered light. The melanosome content of RPE cells changes the polarization state, hence producing a strong RPE-specific signal,
36 allowing effective RPE and drusen segmentation.
37 With advances in SD-OCT image segmentation algorithms, drusen can nowadays be reliably segmented on SD-OCT as well,
14,38 diminishing the need for using PS-OCT for this specific task. Nevertheless, melanin-sensitive PS-OCT would have a value in HRF subtyping, in particular in identifying the HRF that originate from RPE, a subject of our future work.
This pilot study has several limitations, most notably a relatively small sample size. Thus, caution should be exercised when generalizing our findings beyond the analyzed population. It is difficult to identify and recruit patients for such a clinical study, because early and intermediate stages of AMD do not affect patients' vision. We therefore included multiple eyes per patient to increase the overall study eye population, while balancing the statistical analysis for this. In addition, pseudodrusen, a biomarker suspected to play a role in AMD progression,
39 was not used in our study due to difficulties in its automated segmentation. Finally, we identified drusen footprints at baseline and kept them fixed, hence not accounting for possible drusen footprint expansion with time. However, most of the drusen area tends to plateau quickly.
40
Features characterizing drusen are computed from the 2D segmentation maps (
Fig. 5). Accurate layer segmentation of pathologic outer retinas is a complex task, in particular in the presence of sloughed RPE and when HRF are positioned at the layer interfaces (
Fig. 2, top row); hence segmentation errors are possible. We addressed segmentation error robustness in two ways. First, we focused on segmenting large and coarse layers only, that is, ONL and ORB, as opposed to further segmenting RPE and inner and outer segments (IS/OS) within ORB. Second, layer-related features were obtained by averaging thickness maps over the individual drusen footprint, smoothing out local segmentation errors in the process. Finally, machine learning methods are able to identify general patterns and trends in data, and occasional unreasonable feature values are simply treated as outliers.
In this work, we treat confluent drusen as a cluster of individual drusen, while a regression event is likely to affect the entire cluster equally. Alternatively, characterizing them jointly would diminish their heterogeneous aspect. Thus, the exploitation of structural information and interaction with neighboring drusen is still a subject of our future work. In addition, as opposed to using a set of predefined biomarkers, deep learning
41 approaches, which could learn representations of retinal images through a hierarchy of abstraction levels, are a promising path forward.
42
In summary, results of our pilot study show that multidimensional patterns of OCT biomarkers are predictive of incoming drusen regression. Predictive and interpretable models of disease development are highly needed to improve early patient management/screening for patients at risk and increase our knowledge of pathophysiologic mechanisms of AMD progression. The proposed model is the first to allow personalized, objective, and reproducible prediction of drusen regression, which develops within a predictable time frame. It is a promising step forward toward identification of innovative imaging biomarkers of imminent conversion form intermediate to late disease in AMD, and will aid the development and evaluation of new interventions that target intermediate stages of AMD.
Supported in part by the Austrian Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development.
Disclosure: H. Bogunović, None; A. Montuoro, None; M. Baratsits, None; M.G. Karantonis, None; S.M. Waldstein, Bayer AG (C), Novartis (C); F. Schlanitz, None; U. Schmidt-Erfurth, Bayer AG (C), Novartis AG (C)