There are currently no well-established methods to identify and evaluate the mechanisms underlying diabetic macular edema (DME) pathobiology, one of the leading causes of blindness among working-age Americans. As such, the development of pathophysiology-specific therapeutic agents for DME is limited, and the selection of therapies personalized for individual patients remains subjective. Our long-term goal is to develop automated methods that exploit retinal imaging technologies to stratify DME patients into subgroups that reflect specific pathophysiological mechanisms. In turn, we expect that subgrouping according to these mechanisms will facilitate an optimal choice of personalized therapy for each patient. The current paradigm isolates three different pathophysiologic mechanisms, independently or together, as contributing factors to DME: a) capillary endothelial cell dysfunction, b) retinal glial cellular pump dysfunction, and c) retinal pigment epithelium cel pump dysfunction. We propose two interrelated hypotheses based on this paradigm: 1) Fluorescein angiography (FA) and SD-OCT can be quantitatively analyzed using automated algorithms to infer the specific disease mechanism. On FA, the diffuse to focal leakage area (D/F) ratio will reflect the relative predominance of the two pump dysfunction DME subtypes versus the capillary leakage subtype. On SD-OCT, macular thickening and other morphological features indicative of diffuse and focal DME can be identified through layer segmentation. 2) Image analysis using both the D/F ratio and quantitative analysis of SD-OCT will serve as predictive biomarkers for therapeutic responses. More specifically, the FA and SD-OCT markers of diffuse DME will respond better to pharmacotherapy, whereas the markers of focal DME will respond better to focal laser. We will test these hypotheses by pursuit of the following three specific aims:
Aim I : Develop automated software to quantify DME subtype imaging biomarkers on FA and SD-OCT.
Aim II : Use archived DME cases to refine and validate the automated algorithms developed in Aim I.
Aim III : Perform a pilot trial to determine the efficacy of the D/F ratio in predicting anti-VEGF responsiveness in a """"""""treatment na?ve"""""""" and unbiased population. This project is significant because there is an unmet need for therapies personalized to disease subtype. This project will provide objective DME subtyping methods based on FA and SD-OCT and inferential support for different DME mechanisms. This project is innovative and impactful in terms of new technology and new knowledge. We will utilize novel mathematical concepts and develop algorithms to reliably measure DME imaging biomarkers in an automated fashion in a clinical setting. Developed software will be freely distributed to the public and are expected to become the standard methodology used by clinicians to personalize the choice of therapy or by image reading centers to stratify patients for clinical trials of new DME drugs. Finally, we expect that our novel image processing algorithms and their underlying mathematical frameworks will have an immediate impact on a wide spectrum of medical image processing research applications.

Public Health Relevance

Diabetic retinopathy affects approximately 4 million Americans and is the leading cause of blindness among working-age people. However, the specific cause for blindness and optimal treatment for individual diabetic patients is unknown. We will develop novel, computer-aided technology to help better understand the underlying mechanisms of diabetic retinopathy, which in turn is expected to facilitate the optimal choice of therapy personalized for an individual's particular disease mechanism.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
1R01EY022691-01
Application #
8348365
Study Section
Special Emphasis Panel (DPVS)
Program Officer
Shen, Grace L
Project Start
2012-08-01
Project End
2017-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
1
Fiscal Year
2012
Total Cost
$384,751
Indirect Cost
$134,751
Name
Duke University
Department
Ophthalmology
Type
Schools of Medicine
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Bavinger, J Clay; Dunbar, Grace E; Stem, Maxwell S et al. (2016) The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry. Invest Ophthalmol Vis Sci 57:208-17
Stem, M S; Dunbar, G E; Jackson, G R et al. (2016) Glucose variability and inner retinal sensory neuropathy in persons with type 1 diabetes mellitus. Eye (Lond) 30:825-32
Twa, Michael D; Schulle, Krystal L; Chiu, Stephanie J et al. (2016) Validation of Macular Choroidal Thickness Measurements from Automated SD-OCT Image Segmentation. Optom Vis Sci 93:1387-1398
Keller, Brenton; Cunefare, David; Grewal, Dilraj S et al. (2016) Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images. J Biomed Opt 21:76015
Boynton, Grace E; Stem, Maxwell S; Kwark, Leon et al. (2015) Multimodal characterization of proliferative diabetic retinopathy reveals alterations in outer retinal function and structure. Ophthalmology 122:957-67
Chiu, Stephanie J; Allingham, Michael J; Mettu, Priyatham S et al. (2015) Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed Opt Express 6:1172-94
Estrada, Rolando; Allingham, Michael J; Mettu, Priyatham S et al. (2015) Retinal Artery-Vein Classification via Topology Estimation. IEEE Trans Med Imaging 34:2518-34
Fang, Leyuan; Li, Shutao; Kang, Xudong et al. (2015) 3-D Adaptive Sparsity Based Image Compression With Applications to Optical Coherence Tomography. IEEE Trans Med Imaging 34:1306-20
Rabbani, Hossein; Allingham, Michael J; Mettu, Priyatham S et al. (2015) Fully automatic segmentation of fluorescein leakage in subjects with diabetic macular edema. Invest Ophthalmol Vis Sci 56:1482-92
Estrada, Rolando; Tomasi, Carlo; Schmidler, Scott C et al. (2015) Tree Topology Estimation. IEEE Trans Pattern Anal Mach Intell 37:1688-701

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