Radiomics using generative adversarial network enhanced non-contrast computed tomography for gastric cancer diagnosis

作者：Xiaodong Li, Yunpeng Zhao, Mengjie Fang, Xiaoying Huang, Ling Wu, Zhining Liu, Kunshan He, Xu-Yao Zhang, Xuebin Xie, Lei Tang, Jie Tian, Di Dong · 发表于：Physics in Medicine and Biology · 年份：2026 · DOI：10.1088/1361-6560/ae95d6 · 研究领域：Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis、Advanced X-ray and CT Imaging

Abstract Objective. Non-contrast-enhanced computed tomography (NCCT) images have limited tissue resolution for gastric cancer diagnosis, while contrast-enhanced computed tomography (CECT) scans involve risks such as allergic reactions and high radiation exposure. This study proposes an auxiliary diagnostic framework that integrates generative adversarial networks (GANs) with radiomics analysis to enhance the utility of NCCT images and facilitate a more informative imaging-based evaluation of gastric cancer. Approach. NCCT images, CECT images, and corresponding clinical data were collected from 1757 gastric cancer patients across four centers. After Symmetric Normalization-based registration, multiple Pix2Pix-based configurations with attention mechanisms and a composite loss function were developed. Using quantitative metrics including mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), ablation experiments identified the optimal model, which outperformed Pix2Pix, CycleGAN, and Brownian bridge diffusion model (BBDM). Finally, synthetic CECT (SCECT) images were applied in radiomics analysis to predict histological grade (low-grade), Lauren classification (diffuse type), and T stage (T3–T4), thereby demonstrating the feasibility of the framework. Main results. The proposed model significantly outperformed Pix2Pix, CycleGAN, and BBDM on internal and external test sets, as evidenced by quantitativ...