Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning

Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is laborious and data on its impact on therapy of reti...

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Vydáno v:Scientific reports Ročník 7; číslo 1; s. 2928 - 11
Hlavní autoři: Waldstein, Sebastian M., Montuoro, Alessio, Podkowinski, Dominika, Philip, Ana-Maria, Gerendas, Bianca S., Bogunovic, Hrvoje, Schmidt-Erfurth, Ursula
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 07.06.2017
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1564
692/308/575
692/700/1421
Adhesion
Algorithms
Angiogenesis Inhibitors - pharmacology
Angiogenesis Inhibitors - therapeutic use
Artificial intelligence
Female
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
Macula Lutea - diagnostic imaging
Macula Lutea - pathology
Male
Monoclonal antibodies
multidisciplinary
Occlusion
Retina
Retinal Vein Occlusion - diagnostic imaging
Retinal Vein Occlusion - drug therapy
Retinal Vein Occlusion - pathology
Science
Science (multidisciplinary)
Segmentation
Tissue Adhesions
Tomography, Optical Coherence
Treatment Outcome
Vascular endothelial growth factor
Vascular Endothelial Growth Factor A - antagonists & inhibitors
Visual Acuity
ISSN:2045-2322, 2045-2322
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Shrnutí:Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is laborious and data on its impact on therapy of retinal vein occlusion (RVO) are limited. The aim of this study was to (1) develop a fully automated segmentation algorithm for the posterior vitreous boundary and (2) to study the effect of VMA on anti-VEGF therapy for RVO. A combined machine learning/graph cut segmentation algorithm for the posterior vitreous boundary was designed and evaluated. 391 patients with central/branch RVO under standardized ranibizumab treatment for 6/12 months were included in a systematic post-hoc analysis. VMA (70%) was automatically differentiated from non-VMA (30%) using the developed method combined with unsupervised clustering. In this proof-of-principle study, eyes with VMA showed larger BCVA gains than non-VMA eyes (BRVO: 15 ± 12 vs. 11 ± 11 letters, p = 0.02; CRVO: 18 ± 14 vs. 9 ± 13 letters, p < 0.01) and received a similar number of retreatments. However, this association diminished after adjustment for baseline BCVA, also when using more fine-grained VMA classes. Our study illustrates that machine learning represents a promising path to assess imaging biomarkers in OCT.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-02971-y