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  1. Book ; Online ; E-Book: Weiterbildung Radiologie

    Delorme, Stefan / Reimer, Peter / Reith, Wolfgang / Schaefer-Prokop, Cornelia / Schüller-Weidekamm, Claudia / Uhl, Markus

    CME-Beiträge aus: Der Radiologe Juli 2013 - Dezember 2014

    2015  

    Author's details herausgegeben von Stefan Delorme, Peter Reimer, Wolfgang Reith, Cornelia Schäfer-Prokop, Claudia Schüller-Weidekamm, Markus Uhl
    Keywords Medicine ; Radiology
    Subject code 616.0757
    Language German
    Size 1 Online-Ressource (VIII, 254 Seiten)
    Publisher Springer Berlin Heidelberg
    Publishing place Berlin, Heidelberg
    Publishing country Germany
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT019062619
    ISBN 978-3-662-46785-5 ; 9783662467848 ; 3-662-46785-2 ; 3662467844
    DOI 10.1007/978-3-662-46785-5
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

    Full text online

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  2. Article ; Online: Chest Radiography in COVID-19: No Role in Asymptomatic and Oligosymptomatic Disease.

    Schaefer-Prokop, Cornelia / Prokop, Mathias

    Radiology

    2020  Volume 298, Issue 3, Page(s) E156–E157

    MeSH term(s) COVID-19 ; Humans ; Lung ; Radiography ; SARS-CoV-2 ; Singapore
    Language English
    Publishing date 2020-12-08
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2020204038
    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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  3. Book: Radiologische Diagnostik in der Intensivmedizin

    Cejna, Manfred / Schaefer-Prokop, Cornelia

    95 Tabellen

    (RRR, Referenz-Reihe Radiologie)

    2009  

    Author's details Cornelia Schaefer-Prokop. Mit Beitr. von M. Cejna
    Series title RRR, Referenz-Reihe Radiologie
    Keywords Intensivmedizin ; Radiologische Diagnostik ; Bildliche Darstellung
    Subject Diagnostische Radiologie ; Strahlendiagnostik
    Subject code 616.0757
    Language German
    Size XIV, 255 S. : zahlr. Ill., graph. Darst.
    Publisher Thieme
    Publishing place Stuttgart u.a.
    Publishing country Germany
    Document type Book
    HBZ-ID HT015959049
    ISBN 978-3-13-111761-8 ; 3-13-111761-3
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    2009 A 2749 order for loan Magazin

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  4. Article ; Online: Pulmonary functional imaging (PFI): A historical review and perspective.

    Bozovic, Gracijela / Schaefer-Prokop, Cornelia M / Bankier, Alexander A

    Acta radiologica (Stockholm, Sweden : 1987)

    2022  , Page(s) 2841851221076324

    Abstract: PFI Pulmonary Functional Imaging (PFI) refers to visualization and measurement of ventilation, perfusion, gas flow and exchange as well as biomechanics. In this review, we will highlight the historical development of PFI, describing recent advances and ... ...

    Abstract PFI Pulmonary Functional Imaging (PFI) refers to visualization and measurement of ventilation, perfusion, gas flow and exchange as well as biomechanics. In this review, we will highlight the historical development of PFI, describing recent advances and listing the various techniques for PFI offered per modality. Challenges PFI is facing and requirements for PFI from a clinical point of view will be pointed out. Hereby the review is meant as an introduction to PFI.
    Language English
    Publishing date 2022-02-04
    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 10.1177/02841851221076324
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.MO 204: Show issues

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  5. Article ; Online: Beyond the

    Schreuder, Anton / Schaefer-Prokop, Cornelia M

    AJR. American journal of roentgenology

    2021  Volume 217, Issue 4, Page(s) 1011

    Language English
    Publishing date 2021-03-31
    Publishing country United States
    Document type Journal Article ; 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.21.25903
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Management des pulmonalen Rundherdes

    Schaefer-Prokop, Cornelia

    Onkologie up2date

    2020  Volume 2, Issue 04, Page(s) 295–307

    Abstract: Pulmonale Rundherde sind fokale rundliche Lungenparenchymverdichtungen, die über 3 mm und unter 3 cm groß sind. Sie haben je nach ihrer Größe, Form, Dichte und Lage, aber auch je nach Komorbidität und Alter des Patienten eine unterschiedliche Bedeutung. ... ...

    Abstract Pulmonale Rundherde sind fokale rundliche Lungenparenchymverdichtungen, die über 3 mm und unter 3 cm groß sind. Sie haben je nach ihrer Größe, Form, Dichte und Lage, aber auch je nach Komorbidität und Alter des Patienten eine unterschiedliche Bedeutung. Dieser Beitrag beschreibt ihre Diagnostik und die ggf. notwendigen Kontrolluntersuchungen.
    Keywords Rundherd ; Thorax-CT ; perifissurale Verdichtung ; Adenokarzinom ; Lungenkarzinom ; pulmonary nodules ; CT ; perifissural opacity ; lung adenocarcinoma
    Language German
    Publishing date 2020-11-01
    Publisher Georg Thieme Verlag KG
    Publishing place Stuttgart ; New York
    Document type Article
    ISSN 2626-6636 ; 2626-6628
    ISSN (online) 2626-6636
    ISSN 2626-6628
    DOI 10.1055/a-1247-9509
    Database Thieme publisher's database

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  7. Article: Perifissural nodules: ready for application into lung cancer CT screening?

    Schreuder, Anton / Schaefer-Prokop, Cornelia M

    Annals of translational medicine

    2020  Volume 8, Issue 19, Page(s) 1254

    Language English
    Publishing date 2020-10-02
    Publishing country China
    Document type Editorial ; Comment
    ZDB-ID 2893931-1
    ISSN 2305-5847 ; 2305-5839
    ISSN (online) 2305-5847
    ISSN 2305-5839
    DOI 10.21037/atm-20-3384
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals.

    Hendrix, Ward / Rutten, Matthieu / Hendrix, Nils / van Ginneken, Bram / Schaefer-Prokop, Cornelia / Scholten, Ernst T / Prokop, Mathias / Jacobs, Colin

    European radiology

    2023  Volume 33, Issue 11, Page(s) 8279–8288

    Abstract: Objective: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT.: Methods: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period ... ...

    Abstract Objective: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT.
    Methods: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule.
    Results: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017.
    Conclusion: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses.
    Clinical relevance statement: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice.
    Key points: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.
    MeSH term(s) Humans ; Incidence ; Solitary Pulmonary Nodule/diagnostic imaging ; Solitary Pulmonary Nodule/epidemiology ; Multiple Pulmonary Nodules/diagnostic imaging ; Multiple Pulmonary Nodules/epidemiology ; Tomography, X-Ray Computed/methods ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/epidemiology
    Language English
    Publishing date 2023-06-20
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-09826-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3300: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  9. Article ; Online: Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

    Peeters, Dré / Alves, Natália / Venkadesh, Kiran V / Dinnessen, Renate / Saghir, Zaigham / Scholten, Ernst T / Schaefer-Prokop, Cornelia / Vliegenthart, Rozemarijn / Prokop, Mathias / Jacobs, Colin

    European radiology

    2024  

    Abstract: Objective: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules.: Methods and materials: In this retrospective study, we integrated an uncertainty ...

    Abstract Objective: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules.
    Methods and materials: In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty.
    Results: The DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group.
    Conclusion: The integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance.
    Clinical relevance statement: Deep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction.
    Key points: • Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm. • Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse. • Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness.
    Language English
    Publishing date 2024-03-27
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-024-10714-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Die verschiedenen Muster der organisierenden Pneumonie

    Schaefer-Prokop, Cornelia / Eisenhuber, Edith

    Radiologie up2date

    2020  Volume 20, Issue 02, Page(s) 143–156

    Abstract: Die organisierende Pneumonie ist eine unspezifische, reparative Reaktion der Lunge auf verschiedene Noxen. Trigger können u. a. Medikamente, ein Infekt, ein Inhalationstrauma oder eine Lungenreaktion bei systemischer inflammatorischer Erkrankung sein. ... ...

    Abstract Die organisierende Pneumonie ist eine unspezifische, reparative Reaktion der Lunge auf verschiedene Noxen. Trigger können u. a. Medikamente, ein Infekt, ein Inhalationstrauma oder eine Lungenreaktion bei systemischer inflammatorischer Erkrankung sein. Auch wenn die Bildmuster der OP sehr variabel sind, gibt es einige typische Bildzeichen, die für die Diagnose hilfreich sind.
    Keywords organisierende Pneumonie ; Bildgebung ; organizing pneumonia ; imaging
    Language German
    Publishing date 2020-06-01
    Publisher Georg Thieme Verlag KG
    Publishing place Stuttgart ; New York
    Document type Article
    ZDB-ID 2065319-0
    ISSN 1617-8300 ; 1616-0681
    ISSN (online) 1617-8300
    ISSN 1616-0681
    DOI 10.1055/a-1076-3366
    Database Thieme publisher's database

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