Nico Karssemeijer
Overview
Explore the profile of Nico Karssemeijer including associated specialties, affiliations and a list of published articles.
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Articles
149
Citations
3883
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Recent Articles
1.
Moriakov N, Peters J, Mann R, Karssemeijer N, van Dijck J, Broeders M, et al.
Med Image Anal
. 2024 Jul;
97:103269.
PMID: 39024973
Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular...
2.
Lauritzen A, Lillholm M, Lynge E, Nielsen M, Karssemeijer N, Vejborg I
Radiology
. 2024 Jun;
311(3):e232479.
PMID: 38832880
Background Retrospective studies have suggested that using artificial intelligence (AI) may decrease the workload of radiologists while preserving mammography screening performance. Purpose To compare workload and screening performance for two...
3.
Veenhuizen S, van Grinsven S, Laseur I, Bakker M, Monninkhof E, de Lange S, et al.
Eur Radiol
. 2024 Apr;
34(10):6334-6347.
PMID: 38639912
Objectives: Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging...
4.
Lauritzen A, von Euler-Chelpin M, Lynge E, Vejborg I, Nielsen M, Karssemeijer N, et al.
J Med Imaging (Bellingham)
. 2023 Oct;
10(5):054003.
PMID: 37780685
Purpose: Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors,...
5.
Lauritzen A, von Euler-Chelpin M, Lynge E, Vejborg I, Nielsen M, Karssemeijer N, et al.
Radiology
. 2023 Aug;
308(2):e230227.
PMID: 37642571
Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine...
6.
Tan T, Rodriguez-Ruiz A, Zhang T, Xu L, Beets-Tan R, Shen Y, et al.
Insights Imaging
. 2023 Jan;
14(1):10.
PMID: 36645507
Objectives: To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women...
7.
Samperna R, Moriakov N, Karssemeijer N, Teuwen J, Mann R
Diagnostics (Basel)
. 2022 Jul;
12(7).
PMID: 35885594
Automatic breast and fibro-glandular tissue (FGT) segmentation in breast MRI allows for the efficient and accurate calculation of breast density. The U-Net architecture, either 2D or 3D, has already been...
8.
Lauritzen A, Rodriguez-Ruiz A, von Euler-Chelpin M, Lynge E, Vejborg I, Nielsen M, et al.
Radiology
. 2022 Apr;
304(1):41-49.
PMID: 35438561
Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk,...
9.
Wanders A, Mees W, Bun P, Janssen N, Rodriguez-Ruiz A, Dalmis M, et al.
Radiology
. 2022 Feb;
303(2):269-275.
PMID: 35133194
Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of...
10.
Kerschke L, Weigel S, Rodriguez-Ruiz A, Karssemeijer N, Heindel W
Eur Radiol
. 2021 Aug;
32(2):842-852.
PMID: 34383147
Objectives: To evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance. Methods: A total of 2257 full-field digital mammography screening...