Mathias Prokop
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Explore the profile of Mathias Prokop including associated specialties, affiliations and a list of published articles.
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311
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10158
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Recent Articles
11.
Sourlos N, Pelgrim G, Wisselink H, Yang X, de Jonge G, Rook M, et al.
Eur Radiol Exp
. 2024 May;
8(1):63.
PMID: 38764066
Background: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). Methods: Individuals...
12.
Schalekamp S, van Leeuwen K, Calli E, Murphy K, Rutten M, Geurts B, et al.
Eur Radiol
. 2024 May;
34(11):7255-7263.
PMID: 38758252
Introduction: This study investigates the performance of a commercially available artificial intelligence (AI) system to identify normal chest radiographs and its potential to reduce radiologist workload. Methods: Retrospective analysis included...
13.
Peeters D, Alves N, Venkadesh K, Dinnessen R, Saghir Z, Scholten E, et al.
Eur Radiol
. 2024 Mar;
34(10):6639-6651.
PMID: 38536463
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...
14.
Dekker H, Stroomberg G, van der Molen A, Prokop M
Insights Imaging
. 2024 Feb;
15(1):62.
PMID: 38411847
Gadolinium-based contrast agents (GBCA) are essential for diagnostic MRI examinations. GBCA are only used in small quantities on a per-patient basis; however, the acquisition of contrast-enhanced MRI examinations worldwide results...
15.
Gefter W, Prokop M, Seo J, Raoof S, Langlotz C, Hatabu H
Radiology
. 2024 Jan;
310(1):e232778.
PMID: 38259206
No abstract available.
16.
Koco L, Siebers C, Schlooz M, Meeuwis C, Oldenburg H, Prokop M, et al.
Cancers (Basel)
. 2024 Jan;
16(2).
PMID: 38254891
Background: AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs....
17.
Koco L, Balkenende L, Appelman L, Moman M, Sponsel A, Schimanski M, et al.
Invest Radiol
. 2024 Jan;
59(7):538-544.
PMID: 38193779
Objectives: This project aims to model an optimal scanning environment for breast magnetic resonance imaging (MRI) screening based on real-life data to identify to what extent the logistics of breast...
18.
Wang J, Sourlos N, Heuvelmans M, Prokop M, Vliegenthart R, van Ooijen P
Comput Biol Med
. 2023 Dec;
169:107871.
PMID: 38154157
Background: During lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning...
19.
Stam L, Linden S, Oostveen L, Hansen H, Aquarius R, Slump C, et al.
PLoS One
. 2023 Dec;
18(12):e0293353.
PMID: 38134125
Background: Reliably capturing sub-millimeter vessel wall motion over time, using dynamic Computed Tomography Angiography (4D CTA), might provide insight in biomechanical properties of these vessels. This may improve diagnosis, prognosis,...
20.
Klarenbeek S, Aarts M, van den Heuvel M, Prokop M, Tummers M, Schuurbiers-Siebers O
Transl Lung Cancer Res
. 2023 Nov;
12(10):2015-2029.
PMID: 38025812
Background: Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort...