G
Geerard L. Beets
Researcher at Netherlands Cancer Institute
Publications - 311
Citations - 18117
Geerard L. Beets is an academic researcher from Netherlands Cancer Institute. The author has contributed to research in topics: Colorectal cancer & Medicine. The author has an hindex of 61, co-authored 270 publications receiving 14472 citations. Previous affiliations of Geerard L. Beets include Tibotec & Champalimaud Foundation.
Papers
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Journal ArticleDOI
Value of gadofosveset-enhanced MRI and multiplanar reformatting for selecting good responders after chemoradiation for rectal cancer.
Luc A. Heijnen,Monique Maas,M. J. Lahaye,Ulrich Lalji,Doenja M. J. Lambregts,Milou H. Martens,Robert G. Riedl,Geerard L. Beets,Regina G. H. Beets-Tan +8 more
TL;DR: YcT restaging with MRI in rectal cancer is challenging and gadofosveset-enhanced T1W MRI has shown promise for nodal restaging, but this increase was not significant for more accurate clinical decision making.
Journal ArticleDOI
Costs and effects of ultrasonography in the evaluation of palpable breast masses.
Karin Flobbe,Alfons G.H. Kessels,Johan L. Severens,Geerard L. Beets,Harry J. de Koning,Maarten F. von Meyenfeldt,Jos M. A. van Engelshoven +6 more
TL;DR: Investigating the costs and effects of incorporating ultrasonography in the triple assessment of palpable breast masses found that it can result in a reduction of the total costs for the diagnosis and treatment of breast cancer.
Journal ArticleDOI
Defining near-complete response following (chemo)radiotherapy for rectal cancer: systematic review.
Petra Custers,B. M. Geubels,Geerard L. Beets,Doenja M. J. Lambregts,Monique E. van Leerdam,B. Van Triest,Monique Maas +6 more
TL;DR: A systematic review of the terminology, criteria, and features used in the literature to define a clinical near-complete response (near-CR) after chemoradiotherapy for rectal cancer is presented in this article .
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The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation
Hester E. Haak,Hester E. Haak,Xinpei Gao,Monique Maas,Selam Waktola,Sean Benson,Regina G. H. Beets-Tan,Regina G. H. Beets-Tan,Geerard L. Beets,Geerard L. Beets,Monique E. van Leerdam,Jarno Melenhorst,Jarno Melenhorst +12 more
TL;DR: In this article, the authors evaluated the accuracy to assess response with deep learning methods based on endoscopic images in rectal cancer patients after neoadjuvant therapy and found that EfficientNet-B2 was the most successful model with the highest diagnostic performance.
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The evaluation of follow-up strategies of watch-and-wait patients with a complete response after neoadjuvant therapy in rectal cancer.
Hester E. Haak,Hester E. Haak,Jan Zmuc,Doenja M. J. Lambregts,Regina G. H. Beets-Tan,Regina G. H. Beets-Tan,Jarno Melenhorst,Geerard L. Beets,Geerard L. Beets,Monique Maas +9 more
TL;DR: In this paper, the authors analyzed the occurrence and detection of local regrowth (LR) in a watch-and-wait cohort and suggested a more efficient follow-up schedule.