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发表一篇学和医学成像类SCI论文
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Abstract:
Purpose This study aimed to validate a deep learning model’s diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model’s clinical utility for resident training. Methods The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images ( n = 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included. Results The overall area under the receiver operating characteristics (AUROC) of the eight deep learning algorithms was 0.846 (range 0.784–0.884). The best performing model was Xception, with an AUROC of 0.884. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of Xception were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. After introducing the CAD system, underperforming trainees received more help from artificial intelligence than the higher performing trainees ( p = 0.046), and overall confidence levels significantly increased from 3.90 to 4.30 ( p < 0.001). Conclusion The deep learning–based CAD system used in this study for CT diagnosis of cervical LNM from thyroid cancer was clinically validated with an AUROC of 0.884. This approach may serve as a training tool to help resident physicians to gain confidence in diagnosis. Key Points • A deep learning-based CAD system for CT diagnosis of cervical LNM from thyroid cancer was validated using data from a clinical cohort. The AUROC for the eight tested algorithms ranged from 0.784 to 0.884. • Of the eight models, the Xception algorithm was the best performing model for the external validation dataset with 0.884 AUROC. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. • The CAD system exhibited potential to improve diagnostic specificity and accuracy in underperforming trainees (3 of 6 trainees, 50.0%). This approach may have clinical utility as a training tool to help trainees to gain confidence in diagnoses.
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最新影响因子:7.034 | 期刊ISSN:0938-7994 | CiteScore:4.02 |
出版周期:Monthly | 是否OA:YES | 出版年份:1991 |
期刊官方网址:http://www.springer.com/medicine/radiology/journal/330
期刊投稿地址:https://mc.manuscriptcentral.com/eurradiol?PROXY_TO_PAGE_NAME=&PRE_ACTION=&DISPLAY=&CURRENT_PAGE=LOG
自引率:13.30% | 研究方向:医学-核医学 |
出版地区:GERMANY |
SCI期刊coverage:Science Citation Index Expanded(科学引文索引扩展)
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Journal of the European Congress of Radiology (ECR) Official Organ of the European Association of Radiology (EAR) European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Head and Neck Radiology (ESHNR) European Society of Musculoskeletal Radiology (ESSR) European Society of Urogenital Radiology (ESUR) European Society of Thoracic Imaging (ESTI) European Radiology (ER) was founded as the European forum of radiology. It will continuously update the scientific knowledge in radiology by the publication of excellent original papers and state-of-the-art reviews written by leading radiologists. Through its link to the European Congress of Radiology (ECR) European Radiology will improve the communication with the other journals in the world. A thorough and competent refereeing procedure will serve the authors'''''''''''''''''''''''''''''''' need for critical feedback. A well balanced combination of review articles original work short communications from the congress and information on society matters makes ER an indispensable source for current information in this field. European Radiology will be subscribed by all participants of the European Congress of Radiology and will thus reach a regular audience of several thousand readers worldwide. Publishes original articles and state-of-the-art reviews by leading radiologists Offers a balanced combination of review articles, original work, short communications and information on society matters Journal of the European Society of Radiology (ESR) Official publication of a number of professional societies 100% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again Free app available on iTunes and Google Play Store European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
欧洲国会放射学杂志》(ECR)官方机构欧洲放射学协会(EAR)欧洲社会的胃肠道和腹部放射学(ESGAR)欧洲社会的头部和颈部放射学(ESHNR)欧洲社会的肌肉骨骼放射学(几)欧洲社会的泌尿生殖放射学(ESUR)欧洲社会的胸成像(ESTI)欧洲放射学(ER)成立的欧洲放射学论坛。它将不断更新放射学的科学知识,出版优秀的原创论文和一流的放射科医生写的最先进的评论。通过与欧洲放射学大会(ECR)的联系,欧洲放射学将改善与世界其他期刊的交流。一个全面和称职的评审程序将为作者提供批判性的反馈。将来自大会的评论文章、原创作品、简短的交流以及社会问题的信息很好地结合在一起,ER成为该领域当前信息不可或缺的来源。《欧洲放射学》将由欧洲放射学大会的所有与会者订阅,因此将面向全世界的几千名经常读者。 发表原创文章和先进的审查由领先的放射科医生 提供一个平衡的组合,审查文章,原创作品,简短的沟通和社会问题的信息 欧洲放射学会杂志(ESR) 若干专业学会的官方出版物 在接受调查的作者中,100%的人表示,他们肯定会在杂志上发表文章,或者可能会再次在杂志上发表文章 免费应用程序可在iTunes和谷歌播放商店 欧洲放射学(ER)不断更新放射学的科学知识,出版强有力的原创文章和先进的评论,由领先的放射学家撰写。综述文章、原始论文、来自欧洲放射学大会的简短通信和关于社会问题的信息的良好平衡的结合使ER成为该领域当前信息的一个不可或缺的来源。 这是欧洲放射学会的期刊,也是许多学会的官方期刊。 从2004年到2008年,《欧洲放射学补编》在其同伴《欧洲放射学补编》(ISSN 1613-3749)下出版。
大类(学科) | 小类(学科) | 学科排名 |
医学 |
RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING (核医学) 2区 |
20/129 |
年度总发文量 | 年度论文发表量 | 年度综述发表量 |
576 | 571 | 5 |
引文计数(2018)
文献(2015-2017)
5400次引用
1342篇文献
序号 | 类别 | 排名 | 百分位 |
1 |
大类(学科):Medicine
小类(学科):Radiology, Nuclear Medicine and Imaging
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影响因子:13.029
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影响因子:4.912
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影响因子:3.077
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研究方向:医学-全科医学与补充医学
影响因子:0
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研究方向:ONCOLOGY-ENDOCRINOLOGY & METABOLISM
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