Spectropathology-corroborated multimodal quantitative imaging biomarkers for neuroretinal degeneration in diabetic retinopathy

Abstract

Introduction

Image-based early detection for diabetic retinopathy (DR) needs value addition due to lack of well-defined disease-specific quantitative imaging biomarkers (QIBs) for neuroretinal degeneration and spectropathological information at the systemic level. Retinal neurodegeneration is an early event in the pathogenesis of DR. Therefore, development of an integrated assessment method for detecting neuroretinal degeneration using spectropathology and QIBs is necessary for the early diagnosis of DR.

Methods

The present work explored the efficacy of intensity and textural features extracted from optical coherence tomography (OCT) images after selecting a specific subset of features for the precise classification of retinal layers using variants of support vector machine (SVM). Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy were also performed to confirm the spectropathological attributes of serum for further value addition to the OCT, fundoscopy, and fluorescein angiography (FA) findings. The serum metabolomic findings were also incorporated for characterizing retinal layer thickness alterations and vascular asymmetries.

Results

Results suggested that OCT features could differentiate the retinal lesions indicating retinal neurodegeneration with high sensitivity and specificity. OCT, fundoscopy, and FA provided geometrical as well as optical features. NMR revealed elevated levels of ribitol, glycerophosphocholine, and uridine diphosphate N-acetyl glucosamine, while the FTIR of serum samples confirmed the higher expressions of lipids and β-sheet-containing proteins responsible for neoangiogenesis, vascular fragility, vascular asymmetry, and subsequent neuroretinal degeneration in DR.

Conclusion

Our data indicated that disease-specific spectropathological alterations could be the major phenomena behind the vascular attenuations observed through fundoscopy and FA, as well as the variations in the intensity and textural features observed in OCT images. Finally, we propose a model that uses spectropathology corroborated with specific QIBs for detecting neuroretinal degeneration in early diagnosis of DR.

Keywords:

• diabetic retinopathy
• quantitative imaging biomarkers
• QIBs
• spectropathology
• neuroretinal degeneration
• optical coherence tomography
• OCT
• support vector machine
• SVM

Acknowledgments

The authors acknowledge the Council of Scientific and Industrial Research (CSIR), New Delhi, India (award number: 315988) for providing financial support. The authors also acknowledge the Fulbright Foreign Scholarship Board (FFSB), Washington, DC, USA, as well as the United States–India Educational Foundation (USIEF) (grantee ID 15141659), India, for providing financial support for doing research at the Johns Hopkins School of Medicine, MD, USA. The research experience was helpful in synthesizing ideas for this work. There are no relationships between the authors or members of their families mentioned in the research article.

Disclosure

The authors report no conflicts of interest in this work.