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

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Waldstein, Sebastian M ^a   Montuoro, Alessio ^a   Podkowinski, Dominika ^a   Philip, Ana Maria ^a   Gerendas, Bianca S ^a   Bogunovic, Hrvoje ^a   Schmidt Erfurth, Ursula ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

ANGIOGENESIS INHIBITOR; VASCULOTROPIN A;

ANTAGONISTS AND INHIBITORS; DIAGNOSTIC IMAGING; FEMALE; HUMAN; MACHINE LEARNING; MALE; OPTICAL COHERENCE TOMOGRAPHY; PATHOLOGY; RETINA MACULA LUTEA; RETINA VEIN OCCLUSION; TISSUE ADHESION; TREATMENT OUTCOME; VISUAL ACUITY;

ANGIOGENESIS INHIBITORS; FEMALE; HUMANS; MACHINE LEARNING; MACULA LUTEA; MALE; RETINAL VEIN OCCLUSION; TISSUE ADHESIONS; TOMOGRAPHY, OPTICAL COHERENCE; TREATMENT OUTCOME; VASCULAR ENDOTHELIAL GROWTH FACTOR A; VISUAL ACUITY;

EID: 85020401396     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-02971-y     Document Type: Article

References (41)

1. Rogers, S. et al. The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia. Ophthalmology 117, 313-319, doi:10.1016/j.ophtha.2009.07.017 (2010).
2. Brown, D. M. et al. Sustained benefits from ranibizumab for macular edema following branch retinal vein occlusion: 12-month outcomes of a phase III study. Ophthalmology 118, 1594-1602, doi:10.1016/j.ophtha.2011.02.022 (2011).
3. Campochiaro, P. A. et al. Sustained benefits from ranibizumab for macular edema following central retinal vein occlusion: twelvemonth outcomes of a phase III study. Ophthalmology 118, 2041-2049, doi:10.1016/j.ophtha.2011.02.038 (2011).
4. Brown, D. M. et al. Intravitreal aflibercept injection for macular edema secondary to central retinal vein occlusion: 1-year results from the phase 3 COPERNICUS study. Am J Ophthalmol 155, 429-437 e427, doi:10.1016/j.ajo.2012.09.026 (2013).
5. Channa, R., Smith, M. & Campochiaro, P. A. Treatment of macular edema due to retinal vein occlusions. Clin Ophthalmol 5, 705-713, doi:10.2147/OPTH.S7632 (2011).
6. Larsen, M. et al. Individualized Ranibizumab Regimen Driven by Stabilization Criteria for Central Retinal Vein Occlusion: Twelve-Month Results of the CRYSTAL Study. Ophthalmology 123, 1101-1111, doi:10.1016/j.ophtha.2016.01.011 (2016).
7. Tadayoni, R. et al. Individualized Stabilization Criteria-Driven Ranibizumab versus Laser in Branch Retinal Vein Occlusion: Six-Month Results of BRIGHTER. Ophthalmology 123, 1332-1344, doi:10.1016/j.ophtha.2016.02.030 (2016).
8. Kondo, M. et al. Intravitreal injection of bevacizumab for macular edema secondary to branch retinal vein occlusion:results after 12 months and multiple regression analysis. Retina 29, 1242-1248, doi:10.1097/IAE.0b013e3181aa8e20 (2009).
9. Jaissle, G. B. et al. Predictive factors for functional improvement after intravitreal bevacizumab therapy for macular edema due to branch retinal vein occlusion. Graefes Arch Clin Exp Ophthalmol 249, 183-192, doi:10.1007/s00417-010-1470-2 (2011).
10. Schmidt-Erfurth, U. & Waldstein, S. M. A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog Retin Eye Res 50, 1-24, doi:10.1016/j.preteyeres.2015.07.007 (2016).
11. Terao, R. et al. Effect of vitreomacular adhesion on antivascular endothelial growth factor therapy for macular edema secondary to branch retinal vein occlusion. Jpn J Ophthalmol 58, 139-145, doi:10.1007/s10384-013-0302-4 (2014).
12. Mayr-Sponer, U. et al. Influence of the Vitreomacular Interface on Outcomes of Ranibizumab Therapy in Neovascular Age-related Macular Degeneration. Ophthalmology 120, 2620-2629, doi:10.1016/j.ophtha.2013.05.032 (2013).
13. Waldstein, S. M. et al. Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD. Ophthalmology 123, 60-69, doi:10.1016/j.ophtha.2015.09.013 (2016).
14. Houston, S. K. I., Rayess, N., Cohen, M. N., Ho, A. C. & Regillo, C. D. Influence Of Vitreomacular Interface On Anti-Vascular Endothelial Growth Factor Therapy Using Treat And Extend Treatment Protocol For Age-Related Macular Degeneration (Vintrex). Retina 35, 1757-1764, doi:10.1097/iae.0000000000000663 (2015).
15. Sadiq, M. A. et al. Effect of Vitreomacular Adhesion on Treatment Outcomes in the Ranibizumab for Edema of the Macula in Diabetes (READ-3) Study. Ophthalmology 123, 324-329, doi:10.1016/j.ophtha.2015.09.032 (2016).
16. Singh, R. P. et al. A retrospective study of the influence of the vitreomacular interface on macular oedema secondary to retinal vein occlusion. British Journal of Ophthalmology. doi:10.1136/bjophthalmol-2016-309747 (2017).
17. Chakravarthy, U. et al. Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration. Ophthalmology 123, 1731-1736, doi:10.1016/j.ophtha.2016.04.005 (2016).
18. Montuoro, A. et al. Automatic segmentation of the posterior vitreous boundary in retinal optical coherence tomography. Investigative Ophthalmology & Visual Science 56, ARVO E-Abstract 5275 (2015).
19. Montuoro, A. et al. Data driven discovery of anti-VEGF treatment response groups based on fully automatic vitreomacular interface segmentation. Investigative Ophthalmology & Visual Science 57, ARVO E-Abstract 5944 (2016).
20. Montuoro, A., Waldstein, S. M., Gerendas, B. S., Schmidt-Erfurth, U. & Bogunović, H. Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed Opt Express 8, 1874-1888, doi:10.1364/BOE.8.001874 (2017).
21. Sonka, M., Hlavac, V. & Boyle, R. Image processing, analysis, and machine vision. (Cengage Learning, 2014).
22. Wong, T. & Bressler, N. M. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 316, 2366-2367, doi:10.1001/jama.2016.17563 (2016).
23. Munk, M. R. et al. Influence of the vitreomacular interface on the efficacy of intravitreal therapy for uveitis-associated cystoid macular oedema. Acta Ophthalmologica 93, e561-e567, doi:10.1111/aos.12699 (2015).
24. Kim, H. et al. Study of ocular transport of drugs released from an intravitreal implant using magnetic resonance imaging. Annals of biomedical engineering 33, 150-164 (2005).
25. Kim, H., Robinson, S. B. & Csaky, K. G. Investigating the movement of intravitreal human serum albumin nanoparticles in the vitreous and retina. Pharm Res 26, 329-337, doi:10.1007/s11095-008-9745-6 (2009).
26. Holekamp, N. M. The Vitreous Gel: More than Meets the Eye. Am J Ophthalmol 149, 32-36, doi:10.1016/j.ajo.2009.07.036 (2010).
27. Ahn, S. J. et al. Intraocular Pharmacokinetics of Ranibizumab in Vitrectomized Versus Nonvitrectomized Eyes. Investigative Ophthalmology & Visual Science 55, 567-573, doi:10.1167/iovs.13-13054 (2014).
28. Takahashi, H. et al. Effects of posterior vitreous detachment on aqueous humour levels of VEGF and inflammatory cytokines. British Journal of Ophthalmology. doi:10.1136/bjophthalmol-2014-306051 (2015).
29. Quiram, P. A. et al. Microplasmin-induced posterior vitreous detachment affects vitreous oxygen levels. Retina 27, 1090-1096, doi:10.1097/IAE.0b013e3180654229 (2007).
30. Roberts, P., Waldstein, S. M. & Schmidt-Erfurth, U. A focus on the imaging of the retina. Expert Review of Ophthalmology 10, 595-611, doi:10.1586/17469899.2015.1107478 (2015).
31. Kanagasingam, Y. et al. Progress on retinal image analysis for age related macular degeneration. Prog Retin Eye Res 38, 20-42, doi:10.1016/j.preteyeres.2013.10.002 (2014).
32. Kafieh, R., Rabbani, H. & Kermani, S. A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina. Journal of Medical Signals and Sensors 3, 45-60 (2013).
33. Itakura, H. & Kishi, S. Evolution of vitreomacular detachment in healthy subjects. JAMA Ophthalmol 131, 1348-1352, doi:10.1001/jamaophthalmol.2013.4578 (2013).
34. Wang, Q., Ronneberger, O. & Burkhardt, H. Rotational Invariance Based on Fourier Analysis in Polar and Spherical Coordinates. IEEE Trans Pattern Anal Mach Intell 31, 1715-1722, doi:10.1109/TPAMI.2009.29 (2009).
35. Skibbe, H., Wang, Q., Ronneberger, O., Burkhardt, H. & Reisert, M. In IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. 1863-1869 (2009).
36. Schlegl, T., Waldstein, S. M., Schmidt-Erfurth, U. & Langs, G. Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks. Inf Process Med Imaging 24, 437-448 (2015).
37. Montuoro, A., Simader, C., Langs, G. & Schmidt-Erfurth, U. Rotation invariant eigenvessels and auto-context for retinal vessel detection. SPIE Medical Imaging 94131 (2015).
38. Breiman, L. Random Forests. Machine Learning 45, 5-32, doi:10.1023/a:1010933404324 (2001).
39. Waldstein, S. M. et al. Correlation of three-dimensionally quantified intra- and subretinal fluid with visual acuity in neovascular age-related macular degeneration. JAMA Ophthalmology 134, 182-190, doi:10.1001/jamaophthalmol.2015.4948 (2015).
40. Garvin, M. K. et al. Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans Med Imaging 28, 1436-1447, doi:10.1109/TMI.2009.2016958 (2009).
41. Tu, Z. & Bai, X. Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation. IEEE Trans Pattern Anal Mach Intell 32, 1744-1757, doi:10.1109/TPAMI.2009.186 (2010).