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Prediction of OCT images of short-term response to anti-VEGF treatment for diabetic macular edema using different generative adversarial networks

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机构： [1]School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China [2]School of Management, Guangzhou University, Guangzhou, China [3]Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China [4]Department of Ophthalmology, People’s Hospital of Xiajin, Dezhou, China [5]Department of Endocrinology, People’s Hospital of Zoucheng, Jining, China [6]School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China [7]Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China [8]Zibo Central Hospital, Binzhou Medical University, Zibo, Shandong province, China [9]Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China [10]Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

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关键词： Diabetic macular edema Generative adversarial networks Optical coherence tomography Anti-vascular endothelial growth factor Prognostic predictions

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This study sought to assess the predictive performance of optical coherence tomography (OCT) images for the response of diabetic macular edema (DME) patients to anti-vascular endothelial growth factor (VEGF) therapy generated from baseline images using generative adversarial networks (GANs).Patient information, including clinical and imaging data, was obtained from inpatients at the Ophthalmology Department of Qilu Hospital. 715 and 103 pairs of pre-and post-treatment OCT images of DME patients were included in the training and validation sets, respectively. The post-treatment OCT images were used to assess the validity of the generated images. Six different GAN models (CycleGAN, PairGAN, Pix2pixHD, RegGAN, SPADE, UNIT) were applied to predict the efficacy of anti-VEGF treatment by generating OCT images. Independent screening and evaluation experiments were conducted to validate the quality and comparability of images generated by different GAN models.OCT images generated f GAN models exhibited high comparability to the real images, especially for edema absorption. RegGAN exhibited the highest prediction accuracy over the CycleGAN, PairGAN, Pix2pixHD, SPADE, and UNIT models. Further analyses were conducted based on the RegGAN. Most post-therapeutic OCT images (95/103) were difficult to differentiate from the real OCT images by retinal specialists. A mean absolute error of 26.74 ± 21.28 μm was observed for central macular thickness (CMT) between the synthetic and real OCT images.Different generative adversarial networks have different prognostic efficacy for DME, and RegGAN yielded the best performance in our study. Different GAN models yielded good accuracy in predicting the OCT-based response to anti-VEGF treatment at one month. Overall, the application of GAN models can assist clinicians in prognosis prediction of patients with DME to design better treatment strategies and follow-up schedules.Copyright © 2023 Elsevier B.V. All rights reserved.

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出版当年[2022]版：

大类 | 3 区医学

小类 | 4 区肿瘤学

最新[2025]版：

大类 | 3 区医学

小类 | 4 区肿瘤学

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第一作者机构： [1]School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China [2]School of Management, Guangzhou University, Guangzhou, China

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通讯机构： [3]Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China [6]School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China [9]Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China [10]Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China [*1]Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China [*2]Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. [*3]School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China

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Prediction of OCT images of short-term response to anti-VEGF treatment for diabetic macular edema using different generative adversarial networks

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