Pseudo-Contrast-Enhanced US via Enhanced Generative Adversarial Networks for Evaluating Tumor Ablation Efficacy
Chen Chen, Jiabin Yu, Zhikang Xu, Changsong Xu, Zubang Zhou, Jindong Hao, Vicky Yang Wang, Jincao Yao, Lingyan Zhou, Chenke Xu, Mei Song, Qi Zhang, Xiaofang Liu, Lin Sui, Yuqi Yan, Tian Jiang, Yahan Zhou, Yingtianqi Wu, Binggang Xiao, Chenjie Xu, Hongmei Mi, Li Yang, Zhiwei Wu, Qingquan He, Jian Chen, Qi Liu, Dong Xu“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a methodology for creating pseudo-contrast-enhanced US (CEUS) using an enhanced generative adversarial network and evaluate its ability to assess tumor ablation effectiveness. Materials and Methods This retrospective study included 1,030 patients who underwent thyroid nodule ablation across seven centers from January 2020 to April 2023. A generative adversarial network-based model was developed for direct pseudo-CEUS generation from B-mode US and tested on thyroid, breast, and liver ablation datasets. The reliability of pseudo-CEUS was assessed using Structural Similarity Index (SSIM), Color Histogram Correlation (CHC), and Mean Absolute Percentage Error (MAPE) against real CEUS. Additionally, a subjective evaluation system was devised to validate its clinical value. The Wilcoxon signed-rank test was employed to analyze differences in the data. Results The study included 1,030 patients (mean age, 46.9 years ± 12.5; 799 females and 231 males). For internal test set 1, the mean SSIM was 0.89 ± 0.05, while across external test sets 1–6, mean SSIM values ranged from 0.84 ± 0.08 to 0.88 ± 0.04. Subjective assessments affirmed the method’s stability and near-realistic performance in evaluating ablation effectiveness. The thyroid ablation datasets had an average identification score of 0.49 (0.5 indicates indistinguishability), while the similarity average score for all datasets was 4.75 out of 5. Radiologists’ assessments of residual blood supply were nearly consistent, with no differences in defining ablation zones between real and pseudo-CEUS. Conclusion The pseudo-CEUS method demonstrated high similarity to real CEUS in evaluating tumor ablation effectiveness. Published under a CC BY 4.0 license.