Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images.
机构：[1]Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.[2]School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.[3]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510085, China.[4]School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510182, China.深圳市中医院深圳医学信息中心[5]Zibo Central Hospital, Binzhou Medical University, Zibo 256603, China.
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).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.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.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.
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外文
PubmedID：
中科院(CAS)分区：
出版当年[2021]版：
大类|3 区医学
小类|2 区医学：内科
最新[2025]版：
大类|3 区医学
小类|3 区医学：内科
第一作者：
第一作者机构：[1]Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
通讯作者：
推荐引用方式(GB/T 7714)：
Xu Fabao,Liu Shaopeng,Xiang Yifan,et al.Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images.[J].Journal of clinical medicine.2022,11(10):doi:10.3390/jcm11102878.
APA：
Xu Fabao,Liu Shaopeng,Xiang Yifan,Hong Jiaming,Wang Jiawei...&Li Jianqiao.(2022).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,11,(10)
MLA：
Xu Fabao,et al."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 11..10(2022)
