Ruud Pijnappel

Prof dr

UMC Utrecht

Netherlands

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About

Prof. Dr. Ruud Pijnappel is an esteemed faculty member at UMC Utrecht in the Netherlands. His research primarily focuses on advancements in breast imaging technologies and their implications for cancer screening and diagnosis. Recent contributions include studies on the cost-effectiveness of MRI screening for women with dense breast tissue and the automation of MRI examinations using deep learning techniques. He is also actively involved in addressing the challenges faced during the COVID-19 pandemic in relation to breast cancer screening programs.

Articles (10)

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2021

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2021

Collaborators (7)

Debra Ikeda

Professor of Radiology (Breast Imaging), Emerita

Stanford University

UNITED STATES

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Hilde Bosmans

Professor

University of Liege

BELGIUM

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Chantal Van Ongeval

Prof. dr.

Universitaire Ziekenhuizen Leuven

BELGIUM

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Bas H. M. van der Velden

University Medical Center Groningen

NETHERLANDS

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Kenneth G. A. Gilhuijs

NETHERLANDS

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Anetta Bolejko

SWEDEN

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Christiane K. Kuhl

University Hospital RWTH Aachen

GERMANY

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Ruud Pijnappel

Research Interests

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Articles (10)

Added Value of Prereading Screening Mammograms for Breast Cancer by Radiologic Technologists on Early Screening Outcomes.

Cost-Effectiveness of Magnetic Resonance Imaging Screening for Women With Extremely Dense Breast Tissue.

Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.

Axillary lymphadenopathy at the time of COVID-19 vaccination: ten recommendations from the European Society of Breast Imaging (EUSOBI).

Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial.

Impact of the suspension and restart of the Dutch breast cancer screening program on breast cancer incidence and stage during the COVID-19 pandemic.

Supplemental Breast MRI for Women with Extremely Dense Breasts: Results of the Second Screening Round of the DENSE Trial.

Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis.

Diffusion weighted imaging of the breast: Performance of standardized breast tumor tissue selection methods in clinical decision making.

How does image quality affect radiologists' perceived ability for image interpretation and lesion detection in digital mammography?

Collaborators (7)

Debra Ikeda

Hilde Bosmans

Chantal Van Ongeval

Bas H. M. van der Velden

Kenneth G. A. Gilhuijs

Anetta Bolejko

Christiane K. Kuhl

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