Purpose
To assess whether a generative adversarial network (GAN) could synthesize realistic optical coherence tomography (OCT) images that satisfactorily serve as the educational images for retinal specialists, and the training datasets for the classification of various retinal disorders using deep learning (DL).
Methods
The GANs architecture was adopted to synthesize high-resolution OCT images trained on a publicly available OCT dataset, including urgent referrals (37,206 OCT images from eyes with choroidal neovascularization, and 11,349 OCT images from eyes with diabetic macular edema) and nonurgent referrals (8617 OCT images from eyes with drusen, and 51,140 OCT images from normal eyes). Four hundred real and synthetic OCT images were evaluated by two retinal specialists (with over 10 years of clinical retinal experience) to assess image quality. We further trained two DL models on either real or synthetic datasets and compared the performance of urgent versus nonurgent referrals diagnosis tested on a local (1000 images from the public dataset) and clinical validation dataset (278 images from Shanghai Shibei Hospital).
Results
The image quality of real versus synthetic OCT images was similar as assessed by two retinal specialists. The accuracy of discrimination of real versus synthetic OCT images was 59.50% for retinal specialist 1 and 53.67% for retinal specialist 2. For the local dataset, the DL model trained on real (DL_Model_R) and synthetic OCT images (DL_Model_S) had an area under the curve (AUC) of 0.99, and 0.98, respectively. For the clinical dataset, the AUC was 0.94 for DL_Model_R and 0.90 for DL_Model_S.
Conclusions
The GAN synthetic OCT images can be used by clinicians for educational purposes and for developing DL algorithms.
Translational Relevance
The medical image synthesis based on GANs is promising in humans and machines to fulfill clinical tasks.
Background: Gastric cancer (GC) is one of the most common gastrointestinal malignancies worldwide. Emerging evidence indicates that hyperglycemia promotes tumor progression, especially the processes of migration, invasion and epithelial-mesenchymal transition (EMT). However, the underlying mechanisms of GC remain unclear. Method: Data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to detect the expression of glycolysis-related enzymes and EMT-related transcription factors. Small interfering RNA (siRNA) transfection was performed to decrease ENO1 expression. Immunohistochemistry (IHC), Western blot and qRT-PCR analyses were used to measure gene expression at the protein or mRNA level. CCK-8, wound-healing and Transwell assays were used to assess cell proliferation, migration and invasion. Results: Among the glycolysis-related genes, ENO1 was the most significantly upregulated in GC, and its overexpression was correlated with poor prognosis. Hyperglycemia enhanced GC cell proliferation, migration and invasion. ENO1 expression was also upregulated with increasing glucose concentrations. Moreover, decreased ENO1 expression partially reversed the effect of high glucose on the GC malignant phenotype. Snail-induced EMT was promoted by hyperglycemia, and suppressed by ENO1 silencing. Moreover, ENO1 knockdown inhibited the activation of transforming growth factor β (TGF-β) signaling pathway in GC. Conclusions: Our results indicated that hyperglycemia induced ENO1 expression to trigger Snail-induced EMT via the TGF-β/Smad signaling pathway in GC.
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