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  1. Article ; Online: Radiomics and Deep Learning to Predict Pulmonary Nodule Metastasis at CT.

    Sohn, Jae Ho / Fields, Brandon K K

    Radiology

    2024  Volume 311, Issue 1, Page(s) e233356

    MeSH term(s) Humans ; Radiomics ; Deep Learning ; Tomography, X-Ray Computed
    Language English
    Publishing date 2024-04-09
    Publishing country United States
    Document type Editorial
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.233356
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ue I Zs.106: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Connection between partial pressure, volatility, and the Soret effect elucidated using simulations of nonideal supercritical fluid mixtures.

    Fields, Brandon / Schelling, Patrick K

    The Journal of chemical physics

    2024  Volume 160, Issue 8

    Abstract: Building on recent simulation work, it is demonstrated using molecular dynamics simulations of two-component fluid mixtures that the chemical contribution to the Soret effect in two-component nonideal fluid mixtures arises due to differences in how the ... ...

    Abstract Building on recent simulation work, it is demonstrated using molecular dynamics simulations of two-component fluid mixtures that the chemical contribution to the Soret effect in two-component nonideal fluid mixtures arises due to differences in how the partial pressures of the components respond to temperature and density gradients. Further insight is obtained by reviewing the connection between activity and deviations from Raoult's law in the measurement of the vapor pressure of a liquid mixture. A new parameter γsS, defined in a manner similar to the activity coefficient, is used to characterize differences deviations from "ideal" behavior. It is then shown that the difference γ2S-γ1S is predictive of the sign of the Soret coefficient and is correlated to its magnitude. We hence connect the Soret effect to the relative volatility of the components of a fluid mixture, with the more volatile component enriched in the low-density, high-temperature region, and the less volatile component enriched in the high-density, low-temperature region. Because γsS is closely connected to the activity coefficient, this suggests the possibility that measurement of partial vapor pressures might be used to indirectly determine the Soret coefficient. It is proposed that the insight obtained here is quite general and should be applicable to a wide range of materials systems. An attempt is made to understand how these results might apply to other materials systems including interstitials in solids and multicomponent solids with interdiffusion occurring via a vacancy mechanism.
    Language English
    Publishing date 2024-02-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3113-6
    ISSN 1089-7690 ; 0021-9606
    ISSN (online) 1089-7690
    ISSN 0021-9606
    DOI 10.1063/5.0185603
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

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  3. Article ; Online: Screening Breast MRI Effectively Detects Early-Stage Breast Cancer in High-Risk Patients without Prior History of Breast Cancer.

    Fields, Brandon K K / Joe, Bonnie N

    Radiology. Imaging cancer

    2024  Volume 6, Issue 2, Page(s) e249005

    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Early Detection of Cancer ; Breast ; Radiography ; Magnetic Resonance Imaging
    Language English
    Publishing date 2024-02-23
    Publishing country United States
    Document type Journal Article
    ISSN 2638-616X
    ISSN (online) 2638-616X
    DOI 10.1148/rycan.249005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Survival Benefits of Repeated Breast Cancer Screening.

    Fields, Brandon K K / Joe, Bonnie N

    Radiology. Imaging cancer

    2023  Volume 5, Issue 6, Page(s) e239019

    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnosis ; Early Detection of Cancer ; Mammography ; Risk Factors
    Language English
    Publishing date 2023-10-27
    Publishing country United States
    Document type Journal Article
    ISSN 2638-616X
    ISSN (online) 2638-616X
    DOI 10.1148/rycan.239019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.

    Kinkar, Ketki K / Fields, Brandon K K / Yamashita, Mary W / Varghese, Bino A

    Frontiers in radiology

    2024  Volume 3, Page(s) 1326831

    Abstract: Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration ... ...

    Abstract Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.
    Language English
    Publishing date 2024-01-05
    Publishing country Switzerland
    Document type Journal Article ; Review
    ISSN 2673-8740
    ISSN (online) 2673-8740
    DOI 10.3389/fradi.2023.1326831
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Empowering breast cancer diagnosis and radiology practice

    Ketki K. Kinkar / Brandon K. K. Fields / Mary W. Yamashita / Bino A. Varghese

    Frontiers in Radiology, Vol

    advances in artificial intelligence for contrast-enhanced mammography

    2024  Volume 3

    Abstract: Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration ... ...

    Abstract Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.
    Keywords contrast enhanced mammography ; radiomics ; artificial intelligence ; machine learning ; deep learning ; quantitative analysis ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
    Subject code 006
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  7. Article ; Online: Lower extremity infections: Essential anatomy and multimodality imaging findings.

    Matcuk, George R / Skalski, Matthew R / Patel, Dakshesh B / Fields, Brandon K K / Waldman, Leah E / Spinnato, Paolo / Gholamrezanezhad, Ali / Katal, Sanaz

    Skeletal radiology

    2024  

    Abstract: In modern practice, imaging plays an integral role in the diagnosis, evaluation of extent, and treatment planning for lower extremity infections. This review will illustrate the relevant compartment anatomy of the lower extremities and highlight the role ...

    Abstract In modern practice, imaging plays an integral role in the diagnosis, evaluation of extent, and treatment planning for lower extremity infections. This review will illustrate the relevant compartment anatomy of the lower extremities and highlight the role of plain radiographs, CT, US, MRI, and nuclear medicine in the diagnostic workup. The imaging features of cellulitis, abscess and phlegmon, necrotizing soft tissue infection, pyomyositis, infectious tenosynovitis, septic arthritis, and osteomyelitis are reviewed. Differentiating features from noninfectious causes of swelling and edema are discussed.
    Language English
    Publishing date 2024-01-20
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 527592-1
    ISSN 1432-2161 ; 0364-2348
    ISSN (online) 1432-2161
    ISSN 0364-2348
    DOI 10.1007/s00256-024-04567-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1304: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  8. Article ; Online: Imaging of lower extremity infections: predisposing conditions, atypical infections, mimics, and differentiating features.

    Matcuk, George R / Katal, Sanaz / Gholamrezanezhad, Ali / Spinnato, Paolo / Waldman, Leah E / Fields, Brandon K K / Patel, Dakshesh B / Skalski, Matthew R

    Skeletal radiology

    2024  

    Abstract: Imaging evaluation for lower extremity infections can be complicated, especially in the setting of underlying conditions and with atypical infections. Predisposing conditions are discussed, including diabetes mellitus, peripheral arterial disease, ... ...

    Abstract Imaging evaluation for lower extremity infections can be complicated, especially in the setting of underlying conditions and with atypical infections. Predisposing conditions are discussed, including diabetes mellitus, peripheral arterial disease, neuropathic arthropathy, and intravenous drug abuse, as well as differentiating features of infectious versus non-infectious disease. Atypical infections such as viral, mycobacterial, fungal, and parasitic infections and their imaging features are also reviewed. Potential mimics of lower extremity infection including chronic nonbacterial osteomyelitis, foreign body granuloma, gout, inflammatory arthropathies, lymphedema, and Morel-Lavallée lesions, and their differentiating features are also explored.
    Language English
    Publishing date 2024-01-19
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 527592-1
    ISSN 1432-2161 ; 0364-2348
    ISSN (online) 1432-2161
    ISSN 0364-2348
    DOI 10.1007/s00256-024-04589-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1304: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Role of Chest CT in Resource-Driven Healthcare Systems.

    Demirjian, Natalie L / Fields, Brandon K K / Gholamrezanezhad, Ali

    AJR. American journal of roentgenology

    2020  Volume 215, Issue 3, Page(s) W36

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus ; Coronavirus Infections ; Humans ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2 ; Tomography, X-Ray Computed
    Keywords covid19
    Language English
    Publishing date 2020-07-09
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 82076-3
    ISSN 1546-3141 ; 0361-803X ; 0092-5381
    ISSN (online) 1546-3141
    ISSN 0361-803X ; 0092-5381
    DOI 10.2214/AJR.20.23498
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ue I Zs.57: Show issues Location:
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  10. Article: Veillonella

    Lee, Patrick / Fields, Brandon K K / Liang, Tom / Dubé, Michael P / Politano, Seth

    Case reports in hepatology

    2021  Volume 2021, Page(s) 9947213

    Abstract: ... ...

    Abstract Veillonella
    Language English
    Publishing date 2021-10-13
    Publishing country United States
    Document type Case Reports
    ISSN 2090-6587
    ISSN 2090-6587
    DOI 10.1155/2021/9947213
    Database MEDical Literature Analysis and Retrieval System OnLINE

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