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...
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MDPI AG
2022-05-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/10/2878 |
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| author | Fabao Xu Shaopeng Liu Yifan Xiang Jiaming Hong Jiawei Wang Zheyi Shao Rui Zhang Wenjuan Zhao Xuechen Yu Zhiwen Li Xueying Yang Yanshuang Geng Chunyan Xiao Min Wei Weibin Zhai Ying Zhang Shaopeng Wang Jianqiao Li |
| author_facet | Fabao Xu Shaopeng Liu Yifan Xiang Jiaming Hong Jiawei Wang Zheyi Shao Rui Zhang Wenjuan Zhao Xuechen Yu Zhiwen Li Xueying Yang Yanshuang Geng Chunyan Xiao Min Wei Weibin Zhai Ying Zhang Shaopeng Wang Jianqiao Li |
| author_sort | Fabao Xu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T03:39:37Z |
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| id | doaj.art-6b3a9322602d424dbfc80993bf15d91b |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T03:39:37Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-6b3a9322602d424dbfc80993bf15d91b2023-11-23T11:36:17ZengMDPI AGJournal of Clinical Medicine2077-03832022-05-011110287810.3390/jcm11102878Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT ImagesFabao Xu0Shaopeng Liu1Yifan Xiang2Jiaming Hong3Jiawei Wang4Zheyi Shao5Rui Zhang6Wenjuan Zhao7Xuechen Yu8Zhiwen Li9Xueying Yang10Yanshuang Geng11Chunyan Xiao12Min Wei13Weibin Zhai14Ying Zhang15Shaopeng Wang16Jianqiao Li17Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510085, ChinaSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510182, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaZibo Central Hospital, Binzhou Medical University, Zibo 256603, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, ChinaPurpose: 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.https://www.mdpi.com/2077-0383/11/10/2878deep learningdiabetic macular edemagenerative adversarial networksoptical coherence tomography |
| spellingShingle | Fabao Xu Shaopeng Liu Yifan Xiang Jiaming Hong Jiawei Wang Zheyi Shao Rui Zhang Wenjuan Zhao Xuechen Yu Zhiwen Li Xueying Yang Yanshuang Geng Chunyan Xiao Min Wei Weibin Zhai Ying Zhang Shaopeng Wang Jianqiao Li Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images Journal of Clinical Medicine deep learning diabetic macular edema generative adversarial networks optical coherence tomography |
| 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 | deep learning diabetic macular edema generative adversarial networks optical coherence tomography |
| url | https://www.mdpi.com/2077-0383/11/10/2878 |
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