Sebastiaan Breedveld

2.7k total citations
107 papers, 2.1k citations indexed

About

Sebastiaan Breedveld is a scholar working on Radiation, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Sebastiaan Breedveld has authored 107 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Radiation, 60 papers in Pulmonary and Respiratory Medicine and 45 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Sebastiaan Breedveld's work include Advanced Radiotherapy Techniques (88 papers), Radiation Therapy and Dosimetry (44 papers) and Medical Imaging Techniques and Applications (22 papers). Sebastiaan Breedveld is often cited by papers focused on Advanced Radiotherapy Techniques (88 papers), Radiation Therapy and Dosimetry (44 papers) and Medical Imaging Techniques and Applications (22 papers). Sebastiaan Breedveld collaborates with scholars based in Netherlands, United States and Italy. Sebastiaan Breedveld's co-authors include Ben Heijmen, P. Voet, P R M Storchi, Mischa S. Hoogeman, Maarten L.P. Dirkx, A.W. Sharfo, Linda Rossi, Peter C. Levendag, Marleen Keijzer and Shafak Aluwini and has published in prestigious journals such as PLoS ONE, European Journal of Operational Research and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Sebastiaan Breedveld

96 papers receiving 2.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sebastiaan Breedveld Netherlands 25 1.8k 1.3k 1.0k 200 162 107 2.1k
S. Broggi Italy 27 1.5k 0.8× 1.3k 1.0× 1.1k 1.0× 304 1.5× 305 1.9× 113 2.2k
Giovanni Mauro Cattaneo Italy 29 1.7k 0.9× 1.5k 1.2× 1.3k 1.3× 363 1.8× 288 1.8× 84 2.6k
Cristina Garibaldi Italy 26 1.1k 0.6× 941 0.7× 918 0.9× 236 1.2× 85 0.5× 93 1.8k
Yong Yin China 21 608 0.3× 799 0.6× 1.1k 1.1× 255 1.3× 128 0.8× 195 1.8k
Noriyuki Kadoya Japan 24 1.2k 0.6× 941 0.7× 1.1k 1.1× 475 2.4× 59 0.4× 142 1.8k
P. Voet Netherlands 22 1.3k 0.7× 1.2k 0.9× 885 0.9× 252 1.3× 720 4.4× 48 2.2k
Maarten L.P. Dirkx Netherlands 24 1.4k 0.8× 975 0.8× 961 0.9× 330 1.6× 125 0.8× 52 1.7k
C. Rowbottom United Kingdom 21 1.1k 0.6× 813 0.6× 746 0.7× 178 0.9× 199 1.2× 69 1.5k
T. Lacornerie France 26 1.2k 0.7× 1.1k 0.8× 774 0.8× 208 1.0× 237 1.5× 125 2.2k
Geoffrey Zhang United States 28 1.5k 0.8× 1.5k 1.2× 2.3k 2.3× 825 4.1× 62 0.4× 100 3.1k

Countries citing papers authored by Sebastiaan Breedveld

Since Specialization
Citations

This map shows the geographic impact of Sebastiaan Breedveld's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sebastiaan Breedveld with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastiaan Breedveld more than expected).

Fields of papers citing papers by Sebastiaan Breedveld

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sebastiaan Breedveld. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sebastiaan Breedveld. The network helps show where Sebastiaan Breedveld may publish in the future.

Co-authorship network of co-authors of Sebastiaan Breedveld

This figure shows the co-authorship network connecting the top 25 collaborators of Sebastiaan Breedveld. A scholar is included among the top collaborators of Sebastiaan Breedveld based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sebastiaan Breedveld. Sebastiaan Breedveld is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Breedveld, Sebastiaan, Michiel Kroesen, Steven Habraken, et al.. (2024). A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer. Physics in Medicine and Biology. 69(7). 75007–75007. 9 indexed citations
2.
Rossi, Linda, Henrike Westerveld, Miranda E.M.C. Christianen, et al.. (2024). 331: Clinician preferred, fast autoplanning in cervical cancer brachytherapy using BiCycle. Radiotherapy and Oncology. 194. S215–S217. 1 indexed citations
3.
Kolkman‐Deurloo, Inger‐Karine K., Linda Rossi, András Zolnay, et al.. (2024). PHSOR07 Presentation Time: 9:30 AM. Brachytherapy. 23(6). S61–S61. 2 indexed citations
4.
Breedveld, Sebastiaan, et al.. (2024). Dosimetric advantages of adaptive IMPT vs. Enhanced workload and treatment time – A need for automation. Radiotherapy and Oncology. 201. 110548–110548. 3 indexed citations
5.
Nguyen, Dan, Luca Incrocci, A.W. Sharfo, et al.. (2024). Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning. Radiotherapy and Oncology. 203. 110662–110662. 1 indexed citations
6.
Nguyen, Dan, M. Sattler, András Zolnay, et al.. (2024). Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck. Physics in Medicine and Biology. 69(22). 225014–225014.
7.
Rossi, Linda, Sebastiaan Breedveld, & Ben Heijmen. (2023). Per-fraction planning to enhance optimization degrees of freedom compared to the conventional single-plan approach. Physics in Medicine and Biology. 68(17). 175014–175014. 3 indexed citations
8.
Rossi, Linda, et al.. (2023). OC-0130 External validation of automated adaptive planning for EMBRACE II cervical cancer brachytherapy. Radiotherapy and Oncology. 182. S92–S93. 1 indexed citations
9.
Astreinidou, Eleftheria, et al.. (2023). Dosimetric impact of adaptive proton therapy in head and neck cancer – A review. Clinical and Translational Radiation Oncology. 39. 100598–100598. 7 indexed citations
10.
Heijmen, Ben, et al.. (2023). Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans. Acta Oncologica. 62(10). 1194–1200. 6 indexed citations
11.
Heijmen, Ben, et al.. (2022). TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan. Physics in Medicine and Biology. 67(3). 35004–35004. 8 indexed citations
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Rossi, Linda, Tomas Janssen, Peter de Ruiter, et al.. (2021). MR-Linac Radiotherapy – The Beam Angle Selection Problem. Frontiers in Oncology. 11. 717681–717681. 6 indexed citations
16.
Heijmen, Ben, et al.. (2020). Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy. Physics in Medicine and Biology. 65(17). 175011–175011. 4 indexed citations
17.
Breedveld, Sebastiaan, et al.. (2019). Fast and exact Hessian computation for a class of nonlinear functions used in radiation therapy treatment planning. Physics in Medicine and Biology. 64(16). 16NT01–16NT01. 2 indexed citations
18.
Breedveld, Sebastiaan, et al.. (2019). Automated prioritised 3D dose-based MLC segment generation for step-and-shoot IMRT. Physics in Medicine and Biology. 64(16). 165013–165013. 7 indexed citations
19.
Osman, Sarah, Eleftheria Astreinidou, Hans C.J. de Boer, et al.. (2011). IMRT for Image-Guided Single Vocal Cord Irradiation. International Journal of Radiation Oncology*Biology*Physics. 82(2). 989–997. 42 indexed citations
20.
Breedveld, Sebastiaan, et al.. (2010). Fast On-line Plan Adjustment for Adaptive Radiotherapy Evaluated for Prostate and Cervical Cancer. International Journal of Radiation Oncology*Biology*Physics. 78(3). S744–S745. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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