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Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images
Purpose: To generate and evaluate individualized post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of anti-vascular endothelial growth factor (VEGF) therapy for diabetic macular edema (DME) based on pre-therapeutic images using generative adversari...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144043/ https://www.ncbi.nlm.nih.gov/pubmed/35629007 http://dx.doi.org/10.3390/jcm11102878 |
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author | Xu, Fabao Liu, Shaopeng Xiang, Yifan Hong, Jiaming Wang, Jiawei Shao, Zheyi Zhang, Rui Zhao, Wenjuan Yu, Xuechen Li, Zhiwen Yang, Xueying Geng, Yanshuang Xiao, Chunyan Wei, Min Zhai, Weibin Zhang, Ying Wang, Shaopeng Li, Jianqiao |
author_facet | Xu, Fabao Liu, Shaopeng Xiang, Yifan Hong, Jiaming Wang, Jiawei Shao, Zheyi Zhang, Rui Zhao, Wenjuan Yu, Xuechen Li, Zhiwen Yang, Xueying Geng, Yanshuang Xiao, Chunyan Wei, Min Zhai, Weibin Zhang, Ying Wang, Shaopeng Li, Jianqiao |
author_sort | Xu, Fabao |
collection | PubMed |
description | Purpose: To generate and evaluate individualized post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of anti-vascular endothelial growth factor (VEGF) therapy for diabetic macular edema (DME) based on pre-therapeutic images using generative adversarial network (GAN). Methods: Real-world imaging data were collected at the Department of Ophthalmology, Qilu Hospital. A total of 561 pairs of pre-therapeutic and post-therapeutic OCT images of patients with DME were retrospectively included in the training set, 71 pre-therapeutic OCT images were included in the validation set, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. A pix2pixHD method was adopted to predict post-therapeutic OCT images in DME patients that received anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated independently by a screening experiment and an evaluation experiment. Results: The post-therapeutic OCT images generated by the GAN model based on big data were comparable to the actual images, and the response of edema resorption was also close to the ground truth. Most synthetic images (65/71) were difficult to differentiate from the actual OCT images by retinal specialists. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic OCT images and the actual images was 24.51 ± 18.56 μm. Conclusions: The application of GAN can objectively demonstrate the individual short-term response of anti-VEGF therapy one month in advance based on OCT images with high accuracy, which could potentially help to improve treatment compliance of DME patients, identify patients who are not responding well to treatment and optimize the treatment program. |
format | Online Article Text |
id | pubmed-9144043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91440432022-05-29 Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images Xu, Fabao Liu, Shaopeng Xiang, Yifan Hong, Jiaming Wang, Jiawei Shao, Zheyi Zhang, Rui Zhao, Wenjuan Yu, Xuechen Li, Zhiwen Yang, Xueying Geng, Yanshuang Xiao, Chunyan Wei, Min Zhai, Weibin Zhang, Ying Wang, Shaopeng Li, Jianqiao J Clin Med Article Purpose: To generate and evaluate individualized post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of anti-vascular endothelial growth factor (VEGF) therapy for diabetic macular edema (DME) based on pre-therapeutic images using generative adversarial network (GAN). Methods: Real-world imaging data were collected at the Department of Ophthalmology, Qilu Hospital. A total of 561 pairs of pre-therapeutic and post-therapeutic OCT images of patients with DME were retrospectively included in the training set, 71 pre-therapeutic OCT images were included in the validation set, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. A pix2pixHD method was adopted to predict post-therapeutic OCT images in DME patients that received anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated independently by a screening experiment and an evaluation experiment. Results: The post-therapeutic OCT images generated by the GAN model based on big data were comparable to the actual images, and the response of edema resorption was also close to the ground truth. Most synthetic images (65/71) were difficult to differentiate from the actual OCT images by retinal specialists. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic OCT images and the actual images was 24.51 ± 18.56 μm. Conclusions: The application of GAN can objectively demonstrate the individual short-term response of anti-VEGF therapy one month in advance based on OCT images with high accuracy, which could potentially help to improve treatment compliance of DME patients, identify patients who are not responding well to treatment and optimize the treatment program. MDPI 2022-05-19 /pmc/articles/PMC9144043/ /pubmed/35629007 http://dx.doi.org/10.3390/jcm11102878 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Fabao Liu, Shaopeng Xiang, Yifan Hong, Jiaming Wang, Jiawei Shao, Zheyi Zhang, Rui Zhao, Wenjuan Yu, Xuechen Li, Zhiwen Yang, Xueying Geng, Yanshuang Xiao, Chunyan Wei, Min Zhai, Weibin Zhang, Ying Wang, Shaopeng Li, Jianqiao Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images |
title | Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images |
title_full | Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images |
title_fullStr | Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images |
title_full_unstemmed | Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images |
title_short | Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images |
title_sort | prediction of the short-term therapeutic effect of anti-vegf therapy for diabetic macular edema using a generative adversarial network with oct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144043/ https://www.ncbi.nlm.nih.gov/pubmed/35629007 http://dx.doi.org/10.3390/jcm11102878 |
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