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  1. Book ; Audio / Video: Xin HSK ying shi quan jie xi. Wu ji

    Dong, Cui

    2014  

    Author's details edited by Dong Cui
    Language Chinese ; English
    Size IV, 4, 324 S., 1 CD-ROM
    Edition 1. Auflage
    Publisher Beijing language and culture university press
    Publishing place Beijing
    Document type Book ; Audio / Video
    ISBN 9787561937587 ; 756193758X
    Database Former special subject collection: coastal and deep sea fishing

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  2. Book ; Audio / Video: Xin han yu shui ping kao shi mo ni shi ti ji. HSK wu ji

    Dong, Cui

    2010  

    Author's details edited by Dong Cui
    Language Chinese
    Size VI, 290 S., 1 CD-ROM
    Edition 1. Auflage
    Publisher Beijing language and culture university press
    Publishing place Beijing
    Document type Book ; Audio / Video
    ISBN 9787561928790 ; 7561928793
    Database Former special subject collection: coastal and deep sea fishing

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  3. Book: Nai shan yang wu gong hai yang zhi zong he xin ji shu

    Cui, Zhonglin

    (Quan guo wu gong hai shi pin xin dong ji hua cong shu)

    2003  

    Title variant Nai shan yang
    Author's details Cui Zhong Lin zhu bian
    Series title Quan guo wu gong hai shi pin xin dong ji hua cong shu
    Keywords Goats.
    Language Chinese
    Size 2, 8, 260 p., [4] p. of plates :, ill. (some col.) ;, 20 cm.
    Edition Di 1 ban.
    Publisher Zhongguo nong ye chu ban she ; Xin hua shu dian Beijing fa xing suo fa xing
    Publishing place Beijing shi
    Document type Book
    ISBN 7109079961 ; 9787109079960
    Database NAL-Catalogue (AGRICOLA)

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  4. Article: The value of quantitative and a new qualitative color pattern shear wave elastography for the differentiation of ACR TI-RADS 4 or 5 category thyroid nodules measuring ≤10 mm.

    Yi, Ai-Jiao / Yang, Wei-Wei / Cui, Xin-Wu / Dietrich, Christoph F / Wang, Bin

    Frontiers in endocrinology

    2024  Volume 14, Page(s) 1275256

    Abstract: Objective: This study aims to evaluate the diagnostic performance of quantitative shear wave elastography (SWE) and a new qualitative color pattern SWE for the differentiation of benign and malignant American College of Radiology Thyroid Imaging, ... ...

    Abstract Objective: This study aims to evaluate the diagnostic performance of quantitative shear wave elastography (SWE) and a new qualitative color pattern SWE for the differentiation of benign and malignant American College of Radiology Thyroid Imaging, Reporting, and Data System (ACR TI-RADS) 4 or 5 category thyroid nodules measuring ≤10 mm.
    Materials and methods: From May 2020 to July 2022, a total of 237 patients with 270 thyroid nodules were enrolled, and conventional ultrasound and SWE examinations were performed for each patient. Each ACR TI-RADS 4 or 5 category thyroid nodule measuring ≤10 mm was evaluated by quantitative SWE and a new qualitative color pattern SWE. The diagnostic performance of quantitative SWE parameters, the new qualitative color pattern SWE, and the combination of SWE with ACR TI-RADS, respectively, for the differentiation of benign and malignant ACR TI-RADS 4 or 5 category thyroid nodules measuring ≤10 mm was evaluated and compared.
    Results: Among 270 thyroid nodules in 237 patients, 72 (26.67%) thyroid nodules were benign and 198 (73.33%) thyroid nodules were malignant. The qualitative color pattern SWE showed better diagnostic performance than the quantitative SWE parameters. When combining the qualitative color pattern SWE with ACR TI-RADS scores, with the optimal cutoff value of the total points ≥8, the thyroid nodules were considered malignant. The sensitivity, specificity, accuracy, and AUC were 89.90%, 56.94%, 81.11%, and 0.820 (95% CI: 0.768-0.864), respectively. Compared with using qualitative color pattern SWE alone, the combination of qualitative color pattern SWE and ACR TI-RADS had better diagnostic performance, which was significantly different (
    Conclusion: The combination of qualitative SWE color patterns and ACR TI-RADS had high sensitivity and accuracy, which might be a convenient and useful method to differentiate benign and malignant ACR TI-RADS 4 or 5 category thyroid nodules measuring ≤10 mm. It would be helpful for the management of thyroid nodules and improving prognosis.
    MeSH term(s) Humans ; Elasticity Imaging Techniques ; Thyroid Nodule/diagnostic imaging
    Language English
    Publishing date 2024-01-08
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2592084-4
    ISSN 1664-2392
    ISSN 1664-2392
    DOI 10.3389/fendo.2023.1275256
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer.

    Wu, Xinglong / Li, Mengying / Cui, Xin-Wu / Xu, Guoping

    Physics in medicine and biology

    2022  Volume 67, Issue 3

    Abstract: ... ...

    Abstract Objective
    MeSH term(s) Humans ; Lymph Nodes/diagnostic imaging ; Lymph Nodes/pathology ; Lymphatic Metastasis ; Prospective Studies ; ROC Curve ; Retrospective Studies ; Thyroid Neoplasms/diagnostic imaging ; Thyroid Neoplasms/pathology
    Language English
    Publishing date 2022-02-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ac4c47
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 199: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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  6. Article ; Online: ULS4US: universal lesion segmentation framework for 2D ultrasound images.

    Wu, Xinglong / Jiang, Yan / Xing, Hanshuo / Song, Wenbo / Wu, Peiyan / Cui, Xin-Wu / Xu, Guoping

    Physics in medicine and biology

    2023  Volume 68, Issue 16

    Abstract: ... ...

    Abstract Objective
    MeSH term(s) Ultrasonography ; Algorithms ; Diagnosis, Computer-Assisted ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2023-08-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ace09b
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 199: Show issues Location:
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  7. Article ; Online: Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

    Li, Ao / Hu, Yu / Cui, Xin-Wu / Ye, Xin-Hua / Peng, Xiao-Jing / Lv, Wen-Zhi / Zhao, Chong-Ke

    Acta radiologica (Stockholm, Sweden : 1987)

    2024  , Page(s) 2841851231217227

    Abstract: Background: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning.: Purpose: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy.: Material and ... ...

    Abstract Background: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning.
    Purpose: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy.
    Material and methods: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort.
    Results: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all
    Conclusion: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.
    Language English
    Publishing date 2024-02-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 105-3
    ISSN 1600-0455 ; 0284-1851 ; 0349-652X
    ISSN (online) 1600-0455
    ISSN 0284-1851 ; 0349-652X
    DOI 10.1177/02841851231217227
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Predicting Ki-67 expression in hepatocellular carcinoma: nomogram based on clinical factors and contrast-enhanced ultrasound radiomics signatures.

    Zhang, Di / Zhang, Xian-Ya / Lu, Wen-Wu / Liao, Jin-Tang / Zhang, Chao-Xue / Tang, Qi / Cui, Xin-Wu

    Abdominal radiology (New York)

    2024  

    Abstract: Purpose: To develop a contrast-enhanced ultrasound (CEUS) clinic-radiomics nomogram for individualized assessment of Ki-67 expression in hepatocellular carcinoma (HCC).: Methods: A retrospective cohort comprising 310 HCC individuals who underwent ... ...

    Abstract Purpose: To develop a contrast-enhanced ultrasound (CEUS) clinic-radiomics nomogram for individualized assessment of Ki-67 expression in hepatocellular carcinoma (HCC).
    Methods: A retrospective cohort comprising 310 HCC individuals who underwent preoperative CEUS (using SonoVue) at three different centers was partitioned into a training set, a validation set, and an external test set. Radiomics signatures indicating the phenotypes of the Ki-67 were extracted from multiphase CEUS images. The radiomics score (Rad-score) was calculated accordingly after feature selection and the radiomics model was constructed. A clinic-radiomics nomogram was established utilizing multiphase CEUS Rad-score and clinical risk factors. A clinical model only incorporated clinical factors was also developed for comparison. Regarding clinical utility, calibration, and discrimination, the predictive efficiency of the clinic-radiomics nomogram was evaluated.
    Results: Seven radiomics signatures from multiphase CEUS images were selected to calculate the Rad-score. The clinic-radiomics nomogram, comprising the Rad-score and clinical risk factors, indicated a good calibration and demonstrated a better discriminatory capacity compared to the clinical model (AUCs: 0.870 vs 0.797, 0.872 vs 0.755, 0.856 vs 0.749 in the training, validation, and external test set, respectively) and the radiomics model (AUCs: 0.870 vs 0.752, 0.872 vs 0.733, 0.856 vs 0.729 in the training, validation, and external test set, respectively). Furthermore, both the clinical impact curve and the decision curve analysis displayed good clinical application of the nomogram.
    Conclusion: The clinic-radiomics nomogram constructed from multiphase CEUS images and clinical risk parameters can distinguish Ki-67 expression in HCC patients and offer useful insights to guide subsequent personalized treatment.
    Language English
    Publishing date 2024-03-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2839786-1
    ISSN 2366-0058 ; 2366-004X
    ISSN (online) 2366-0058
    ISSN 2366-004X
    DOI 10.1007/s00261-024-04191-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1262: Show issues Location:
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  9. Article ; Online: Erratum for: Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

    Zhou, Li-Qiang / Wu, Xing-Long / Huang, Shu-Yan / Wu, Ge-Ge / Ye, Hua-Rong / Wei, Qi / Bao, Ling-Yun / Deng, You-Bin / Li, Xing-Rui / Cui, Xin-Wu / Dietrich, Christoph F

    Radiology

    2024  Volume 310, Issue 3, Page(s) e249009

    Language English
    Publishing date 2024-03-26
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.249009
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: A narrative review on the application of artificial intelligence in renal ultrasound.

    Xu, Tong / Zhang, Xian-Ya / Yang, Na / Jiang, Fan / Chen, Gong-Quan / Pan, Xiao-Fang / Peng, Yue-Xiang / Cui, Xin-Wu

    Frontiers in oncology

    2024  Volume 13, Page(s) 1252630

    Abstract: Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. ... ...

    Abstract Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.
    Language English
    Publishing date 2024-03-01
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2023.1252630
    Database MEDical Literature Analysis and Retrieval System OnLINE

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