{"title":"Pseudo-Contrast-Enhanced US via Enhanced Generative Adversarial Networks for Evaluating Tumor Ablation Efficacy.","authors":"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","doi":"10.1148/ryai.240370","DOIUrl":"https://doi.org/10.1148/ryai.240370","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. 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.</i> 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.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240370"},"PeriodicalIF":8.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhao Shi, Bin Hu, Mengjie Lu, Manting Zhang, Haiting Yang, Bo He, Jiyao Ma, Chunfeng Hu, Li Lu, Sheng Li, Shiyu Ren, Yonggao Zhang, Jun Li, Mayidili Nijiati, Jia-Ke Dong, Hao Wang, Zhen Zhou, Fan Dong Zhang, Chengwei Pan, Yizhou Yu, Zijian Chen, Chang Sheng Zhou, Yongyue Wei, Junlin Zhou, Long Jiang Zhang
{"title":"Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.","authors":"Zhao Shi, Bin Hu, Mengjie Lu, Manting Zhang, Haiting Yang, Bo He, Jiyao Ma, Chunfeng Hu, Li Lu, Sheng Li, Shiyu Ren, Yonggao Zhang, Jun Li, Mayidili Nijiati, Jia-Ke Dong, Hao Wang, Zhen Zhou, Fan Dong Zhang, Chengwei Pan, Yizhou Yu, Zijian Chen, Chang Sheng Zhou, Yongyue Wei, Junlin Zhou, Long Jiang Zhang","doi":"10.1148/ryai.240140","DOIUrl":"https://doi.org/10.1148/ryai.240140","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. 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.</i> Purpose To evaluate a Sham-AI model acting as a placebo control for a Standard-AI model for intracranial aneurysm diagnosis. Materials and Methods This retrospective crossover, blinded, multireader multicase study was conducted from November 2022 to March 2023. A Sham-AI model with near-zero sensitivity and similar specificity to a Standard-AI model was developed using 16,422 CT angiography (CTA) examinations. Digital subtraction angiography-verified CTA examinations from four hospitals were collected, half of which were processed by Standard-AI and the others by Sham-AI to generate Sequence A; Sequence B was generated reversely. Twenty-eight radiologists from seven hospitals were randomly assigned with either sequence, and then assigned with the other sequence after a washout period. The diagnostic performances of radiologists alone, radiologists with Standard-AI-assisted, and radiologists with Sham-AI-assisted were compared using sensitivity and specificity, and radiologists' susceptibility to Sham-AI suggestions was assessed. Results The testing dataset included 300 patients (median age, 61 (IQR, 52.0-67.0) years; 199 male), 50 of which had aneurysms. Standard-AI and Sham-AI performed as expected (sensitivity: 96.0% versus 0.0%, specificity: 82.0% versus 76.0%). The differences in sensitivity and specificity between Standard-AI-assisted and Sham-AIassisted readings were +20.7% (95%CI: 15.8%-25.5%, superiority) and 0.0% (95%CI: -2.0%-2.0%, noninferiority), respectively. The difference between Sham-AI-assisted readings and radiologists alone was-2.6% (95%CI: -3.8%--1.4%, noninferiority) for both sensitivity and specificity. 5.3% (44/823) of true-positive and 1.2% (7/577) of false-negative results of radiologists alone were changed following Sham-AI suggestions. Conclusion Radiologists' diagnostic performance was not compromised when aided by the proposed Sham-AI model compared with their unassisted performance. Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240140"},"PeriodicalIF":8.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ian A Selby, Eduardo González Solares, Anna Breger, Michael Roberts, Lorena Escudero Sánchez, Judith Babar, James H F Rudd, Nicholas A Walton, Evis Sala, Carola-Bibiane Schönlieb, Jonathan R Weir-McCall
{"title":"A Pipeline for Automated Quality Control of Chest Radiographs.","authors":"Ian A Selby, Eduardo González Solares, Anna Breger, Michael Roberts, Lorena Escudero Sánchez, Judith Babar, James H F Rudd, Nicholas A Walton, Evis Sala, Carola-Bibiane Schönlieb, Jonathan R Weir-McCall","doi":"10.1148/ryai.240003","DOIUrl":"https://doi.org/10.1148/ryai.240003","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. 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.</i> This article presents a suite of quality control tools for chest radiographs based on traditional and artificial intelligence methods, developed and tested with data from 39 centers in 7 countries. Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240003"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}