A.W. Sharfo

537 total citations
26 papers, 410 citations indexed

About

A.W. Sharfo is a scholar working on Radiation, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, A.W. Sharfo has authored 26 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Radiation, 12 papers in Pulmonary and Respiratory Medicine and 9 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in A.W. Sharfo's work include Advanced Radiotherapy Techniques (18 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Prostate Cancer Diagnosis and Treatment (4 papers). A.W. Sharfo is often cited by papers focused on Advanced Radiotherapy Techniques (18 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Prostate Cancer Diagnosis and Treatment (4 papers). A.W. Sharfo collaborates with scholars based in Netherlands, Germany and Italy. A.W. Sharfo's co-authors include Ben Heijmen, Sebastiaan Breedveld, Mischa S. Hoogeman, P. Voet, Maarten L.P. Dirkx, Jan Willem Mens, Linda Rossi, S.T. Heijkoop, Shafak Aluwini and Yvette Seppenwoolde and has published in prestigious journals such as PLoS ONE, International Journal of Radiation Oncology*Biology*Physics and Radiotherapy and Oncology.

In The Last Decade

A.W. Sharfo

24 papers receiving 406 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A.W. Sharfo Netherlands 11 340 212 194 59 51 26 410
Georgios Ioannidis Greece 5 298 0.9× 210 1.0× 142 0.7× 72 1.2× 36 0.7× 11 373
Görkem Güngör Türkiye 10 269 0.8× 199 0.9× 216 1.1× 41 0.7× 13 0.3× 35 375
Qianyi Xu United States 13 380 1.1× 293 1.4× 245 1.3× 84 1.4× 41 0.8× 43 553
Monica Serban Canada 12 284 0.8× 201 0.9× 210 1.1× 88 1.5× 98 1.9× 39 435
Mete Yeğiner Türkiye 10 210 0.6× 263 1.2× 148 0.8× 108 1.8× 96 1.9× 26 484
T Ganesh India 10 249 0.7× 134 0.6× 222 1.1× 43 0.7× 15 0.3× 61 359
Petra Trnková Austria 13 282 0.8× 203 1.0× 147 0.8× 72 1.2× 85 1.7× 34 403
Ans Swinnen Netherlands 11 335 1.0× 335 1.6× 213 1.1× 88 1.5× 93 1.8× 24 520
C. South United Kingdom 11 465 1.4× 357 1.7× 436 2.2× 61 1.0× 26 0.5× 32 672
Brian Napolitano United States 9 213 0.6× 145 0.7× 161 0.8× 63 1.1× 18 0.4× 14 374

Countries citing papers authored by A.W. Sharfo

Since Specialization
Citations

This map shows the geographic impact of A.W. Sharfo'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 A.W. Sharfo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A.W. Sharfo more than expected).

Fields of papers citing papers by A.W. Sharfo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A.W. Sharfo. 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 A.W. Sharfo. The network helps show where A.W. Sharfo may publish in the future.

Co-authorship network of co-authors of A.W. Sharfo

This figure shows the co-authorship network connecting the top 25 collaborators of A.W. Sharfo. A scholar is included among the top collaborators of A.W. Sharfo 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 A.W. Sharfo. A.W. Sharfo 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.
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
2.
Godart, Jérémy, A.W. Sharfo, Sebastiaan Breedveld, et al.. (2023). The impact of bone marrow sparing on organs at risk dose for cervical cancer: a Pareto front analysis. Frontiers in Oncology. 13. 1138433–1138433. 4 indexed citations
3.
4.
Sharfo, A.W., Linda Rossi, Maarten L.P. Dirkx, et al.. (2021). Complementing Prostate SBRT VMAT With a Two-Beam Non-Coplanar IMRT Class Solution to Enhance Rectum and Bladder Sparing With Minimum Increase in Treatment Time. Frontiers in Oncology. 11. 620978–620978. 5 indexed citations
5.
Sharfo, A.W., et al.. (2021). Pre-clinical validation of a novel system for fully-automated treatment planning. Radiotherapy and Oncology. 158. 253–261. 28 indexed citations
6.
7.
Sharfo, A.W., Ruud Wiggenraad, J. van Santvoort, et al.. (2019). Lower doses to hippocampi and other brain structures for skull-base meningiomas with intensity modulated proton therapy compared to photon therapy. Radiotherapy and Oncology. 142. 147–153. 26 indexed citations
8.
Sharfo, A.W., Florian Stieler, Ben Heijmen, et al.. (2018). Automated VMAT planning for postoperative adjuvant treatment of advanced gastric cancer. Radiation Oncology. 13(1). 74–74. 20 indexed citations
9.
Sharfo, A.W., Maarten L.P. Dirkx, Sebastiaan Breedveld, et al.. (2018). Late toxicity in the randomized multicenter HYPRO trial for prostate cancer analyzed with automated treatment planning. Radiotherapy and Oncology. 128(2). 349–356. 18 indexed citations
10.
Sharfo, A.W., Maarten L.P. Dirkx, Sebastiaan Breedveld, Alejandra Méndèz Romero, & Ben Heijmen. (2017). VMAT plus a few computer-optimized non-coplanar IMRT beams (VMAT+) tested for liver SBRT. Radiotherapy and Oncology. 123(1). 49–56. 22 indexed citations
11.
12.
Buschmann, Martin, A.W. Sharfo, J. Penninkhof, et al.. (2017). Automated volumetric modulated arc therapy planning for whole pelvic prostate radiotherapy. Strahlentherapie und Onkologie. 194(4). 333–342. 44 indexed citations
13.
Sharfo, A.W., Sebastiaan Breedveld, Shafak Aluwini, et al.. (2017). OC-0251: Late toxicity in HYPRO randomized trial analyzed by automated planning and intrinsic NTCP modelling. Radiotherapy and Oncology. 123. S126–S126. 1 indexed citations
14.
Sharfo, A.W., Sebastiaan Breedveld, P. Voet, et al.. (2016). Validation of Fully Automated VMAT Plan Generation for Library-Based Plan-of-the-Day Cervical Cancer Radiotherapy. PLoS ONE. 11(12). e0169202–e0169202. 58 indexed citations
15.
Creutzberg, Carien L., et al.. (2016). Which cervical and endometrial cancer patients will benefit most from intensity-modulated proton therapy?. Radiotherapy and Oncology. 120(3). 397–403. 18 indexed citations
16.
Heijkoop, S.T., Henrike Westerveld, Nina Bijker, et al.. (2016). Optimal Patient Positioning (Prone Versus Supine) for VMAT in Gynecologic Cancer: A Dosimetric Study on the Effect of Different Margins. International Journal of Radiation Oncology*Biology*Physics. 96(2). 432–439. 8 indexed citations
17.
Sharfo, A.W., P. Voet, D. Fransen, et al.. (2016). Fully automated treatment plan generation using ErasmusiCycle - the Rotterdam experience. Radiotherapy and Oncology. 119. S145–S145. 1 indexed citations
18.
Sharfo, A.W., P. Voet, Sebastiaan Breedveld, et al.. (2015). Comparison of VMAT and IMRT strategies for cervical cancer patients using automated planning. Radiotherapy and Oncology. 114(3). 395–401. 76 indexed citations
19.
Heijmen, Ben, A.W. Sharfo, Sebastiaan Breedveld, et al.. (2015). Fully Automated Treatment Plan Generation for Online Adaptive Radiation Therapy in Locally Advanced Cervical Cancer. International Journal of Radiation Oncology*Biology*Physics. 93(3). S216–S216. 1 indexed citations
20.
Heijmen, Ben, P. Voet, A.W. Sharfo, et al.. (2014). Fully automated treatment plan generation in daily routine. Physica Medica. 30. e2–e2. 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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