Quantifying the Quality of GAN-Synthesized Images: A Study on Synthesizing Post-Contrast Sequences from Pre-Contrast Sequences in Breast DCE-MRI

Recent research has shown that Generative Adversarial Networks (GANs) can generate highly realistic breast images through synthesis. Nevertheless, most of these studies assessed image quality solely through visual appraisal or reader studies, lacking quantitative analysis for specific clinical applications. This study aimed to quantitatively assess the quality of GAN-generated breast MRI images in predicting breast cancer recurrence risk. To achieve this, we developed a GAN model to synthesize the first post-contrast sequences from pre-contrast MRI sequences, utilizing an in-house dataset comprising 200 patients with confirmed breast cancer and available breast Dynamic Contrast-Enhanced MRI (DCE-MRI) staging data. In our study, we conducted a statistical analysis of radiomic features, revealing that among the 98 features assessed, 83 showed no significant differences (with p-values greater than 0.05) when comparing synthesized images with real images. Additionally, we employed a Lasso-Regression model to predict the Oncotype DX recurrence risk score. This analysis indicated that the predictive results for recurrence risk, derived from both real and synthesized images, did not exhibit significant differences, underscoring the comparability of synthesized images in this context.