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- 저자 : Yuxuan Qiu (검색결과 3 건)
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Yuxuan Qiu,Zhichao Xing,Qianru Yang,Yan Luo 대한초음파의학회 2022 ULTRASONOGRAPHY Vol.41 No.2
Purpose: This study aimed to assess the diagnostic performance of supersonic impulse (SSI) elastography in differentiating malignant and benign cervical lymph nodes.Methods: The Medline, Embase, and Cochrane Central databases were searched until December 1, 2020. Two different reviewers checked the studies and extracted the data. The diagnostic yields were quantitatively synthesized using a Bayesian bivariate model with an integrated nested Laplace approximation in R.Results: In total, 590 patients with 892 cervical lymph nodes who underwent SSI elastography were included. The total prevalence of malignancy was 33.7% (301/892), and the four elastic modulus values (mean, maximum, minimum, and standard deviation) were significantly different between malignant and benign lymph nodes. For the mean elastic modulus, the summary estimates for sensitivity and specificity were 0.720 (95% credible interval [CrI], 0.592 to 0.824) and 0.877 (95% CrI, 0.727 to 0.969), respectively. The estimated area under the curve (AUC) was 0.845 (95% CrI, 0.672 to 0.914). For the maximum elastic modulus, the sensitivity and specificity were estimated to be 0.809 (95% CrI, 0.698 to 0.899) and 0.816 (95% CrI, 0.643 to 0.924), respectively. The estimated AUC was 0.834 (95% CrI, 0.579 to 0.938). The minimum and standard deviation of the elastic modulus and the outcomes of the positive and negative likelihood ratio, diagnostic odds ratio, and risk difference were also calculated.Conclusion: SSI elastography is an acceptable imaging technique for diagnosing malignant cervical lymph nodes, and it can play a complementary role today. Both maximum and mean elastic modulus values should be taken into consideration to make a clinical judgment.
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Zhichao Xing,Yuxuan Qiu,Jingqiang Zhu,Anping Su,Wenshuang Wu 대한초음파의학회 2023 ULTRASONOGRAPHY Vol.42 No.4
Purpose: Ultrasound (US) risk stratification systems (RSSs) are increasingly being utilized for the optimal management of thyroid nodules, including those with indeterminate cytology. The goal of this study was to evaluate the category-based diagnostic performance of US RSSs in identifying malignancy in indeterminate nodules. Methods: This systematic review and meta-analysis was registered on PROSPERO (CRD42021266195). PubMed, EMBASE, and Web of Science were searched through December 1, 2022. Original articles reporting data on the performance of US RSSs for indeterminate nodules were included. The numbers of nodules classified as true negative, true positive, false negative, and false positive were extracted. Results: Thirty-three studies evaluating 7,225 indeterminate thyroid nodules were included. The diagnostic accuracy was quantitatively synthesized using a Bayesian bivariate model based on the integrated nested Laplace approximation in R. For the intermediate- to high-risk category, the sensitivity levels of the American College of Radiology, the American Thyroid Association, the European Thyroid Association, the Korean Thyroid Association/Korean Society of Thyroid Radiology, and Kwak et al. were found to be 0.80, 0.72, 0.76, 0.96, and 0.97, respectively. The corresponding specificity measurements were 0.36, 0.50, 0.49, 0.28, and 0.17. Furthermore, for the high-risk category, the sensitivity values were 0.40, 0.46, 0.55, 0.47, and 0.10, while the specificity levels were 0.91, 0.90, 0.71, 0.91, and 0.99, respectively. Conclusion: The overall diagnostic performance of the US RSSs was moderate in the differentiation of indeterminate nodules.
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Lu Yi,Wu Jiachuan,Hu Minhui,Zhong Qinghua,Er Limian,Shi Huihui,Cheng Weihui,Chen Ke,Liu Yuan,Qiu Bingfeng,Xu Qiancheng,Lai Guangshun,Wang Yufeng,Luo Yuxuan,Mu Jinbao,Zhang Wenjie,Zhi Min,Sun Jiachen 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.6
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
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