Simone Pieplenbosch

486 total citations
14 papers, 219 citations indexed

About

Simone Pieplenbosch is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Simone Pieplenbosch has authored 14 papers receiving a total of 219 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Pathology and Forensic Medicine and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Simone Pieplenbosch's work include Medical Imaging Techniques and Applications (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Lymphoma Diagnosis and Treatment (5 papers). Simone Pieplenbosch is often cited by papers focused on Medical Imaging Techniques and Applications (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Lymphoma Diagnosis and Treatment (5 papers). Simone Pieplenbosch collaborates with scholars based in Netherlands, Germany and United Kingdom. Simone Pieplenbosch's co-authors include Ronald Boellaard, Otto S. Hoekstra, Josée M. Zijlstra, Coreline N. Burggraaff, Gerben J.C. Zwezerijnen, Henrica C. W. de Vet, Yvonne W. S. Jauw, Maqsood Yaqub, Conny J. van der Laken and Daniela E. Oprea‐Lager and has published in prestigious journals such as Scientific Reports, International Journal of Molecular Sciences and Annals of the Rheumatic Diseases.

In The Last Decade

Simone Pieplenbosch

14 papers receiving 219 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simone Pieplenbosch Netherlands 9 149 76 61 39 24 14 219
Julia Chalaye France 11 116 0.8× 30 0.4× 61 1.0× 49 1.3× 11 0.5× 23 323
Xavier Palard-Novello France 9 108 0.7× 24 0.3× 74 1.2× 48 1.2× 16 0.7× 30 225
Bernard Crawford United States 10 93 0.6× 86 1.1× 208 3.4× 45 1.2× 24 1.0× 13 380
Feisal Bunkheila Italy 7 116 0.8× 60 0.8× 115 1.9× 31 0.8× 16 0.7× 14 256
Toshitaka Okuno Japan 10 72 0.5× 53 0.7× 53 0.9× 110 2.8× 31 1.3× 26 254
Lorenzo Falcinelli Italy 10 102 0.7× 30 0.4× 79 1.3× 61 1.6× 15 0.6× 28 266
V. Morillo Spain 9 66 0.4× 28 0.4× 118 1.9× 132 3.4× 39 1.6× 34 317
T.Y. Andraos United States 8 102 0.7× 101 1.3× 98 1.6× 60 1.5× 3 0.1× 33 244
Daniele Sances Italy 7 64 0.4× 175 2.3× 95 1.6× 127 3.3× 14 0.6× 9 470
Chiaki Takasawa Japan 7 238 1.6× 65 0.9× 48 0.8× 31 0.8× 23 1.0× 10 335

Countries citing papers authored by Simone Pieplenbosch

Since Specialization
Citations

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

Fields of papers citing papers by Simone Pieplenbosch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Simone Pieplenbosch. 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 Simone Pieplenbosch. The network helps show where Simone Pieplenbosch may publish in the future.

Co-authorship network of co-authors of Simone Pieplenbosch

This figure shows the co-authorship network connecting the top 25 collaborators of Simone Pieplenbosch. A scholar is included among the top collaborators of Simone Pieplenbosch 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 Simone Pieplenbosch. Simone Pieplenbosch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Golla, Sandeep S.V., Jakoba J. Eertink, Sanne E. Wiegers, et al.. (2023). Sensitivity of an AI method for [18F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols. EJNMMI Research. 13(1). 88–88. 1 indexed citations
2.
Pieplenbosch, Simone, et al.. (2023). POS0153 MACROPHAGE [11C]-DPA-713 PET-CT IMAGING TO PREDICT EARLY ANTI-TNF TREATMENT OUTCOME IN RHEUMATOID ARTHRITIS. Annals of the Rheumatic Diseases. 82. 298–298. 1 indexed citations
3.
Eertink, Jakoba J., Sandeep S.V. Golla, Sanne E. Wiegers, et al.. (2022). Combatting the effect of image reconstruction settings on lymphoma [18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Research. 12(1). 44–44. 14 indexed citations
4.
Boellaard, Ronald, Bart de Vries, Joyce van Sluis, et al.. (2022). SEMI-AUTOMATED AI BASED ORGAN DELINEATION ON LOW DOSE CT TO FACILITATE PET RADIOTRACER BIODISTRIBUTION MEASUREMENTS. Physica Medica. 104. S126–S126. 2 indexed citations
5.
Eertink, Jakoba J., Gerben J.C. Zwezerijnen, Sanne E. Wiegers, et al.. (2022). Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma. Blood Advances. 7(2). 214–223. 13 indexed citations
6.
Zwezerijnen, Gerben J.C., Jakoba J. Eertink, Coreline N. Burggraaff, et al.. (2021). Interobserver Agreement on Automated Metabolic Tumor Volume Measurements of Deauville Score 4 and 5 Lesions at Interim 18F-FDG PET in Diffuse Large B-Cell Lymphoma. Journal of Nuclear Medicine. 62(11). 1531–1536. 9 indexed citations
7.
Burggraaff, Coreline N., Jakoba J. Eertink, Pieternella J. Lugtenburg, et al.. (2021). 18F-FDG PET Improves Baseline Clinical Predictors of Response in Diffuse Large B-Cell Lymphoma: The HOVON-84 Study. Journal of Nuclear Medicine. 63(7). 1001–1007. 17 indexed citations
8.
Yaqub, Maqsood, et al.. (2020). Quantitative Assessment of Arthritis Activity in Rheumatoid Arthritis Patients Using [11C]DPA-713 Positron Emission Tomography. International Journal of Molecular Sciences. 21(9). 3137–3137. 6 indexed citations
9.
Yaqub, Maqsood, Stefan Bruijnen, Simone Pieplenbosch, et al.. (2020). First in man study of [18F]fluoro-PEG-folate PET: a novel macrophage imaging technique to visualize rheumatoid arthritis. Scientific Reports. 10(1). 1047–1047. 52 indexed citations
10.
Burggraaff, Coreline N., Simone Pieplenbosch, Sally F. Barrington, et al.. (2020). Optimizing Workflows for Fast and Reliable Metabolic Tumor Volume Measurements in Diffuse Large B Cell Lymphoma. Molecular Imaging and Biology. 22(4). 1102–1110. 38 indexed citations
11.
Pfaehler, Elisabeth, Liesbet Mesotten, Ivan Zhovannik, et al.. (2020). Plausibility and redundancy analysis to select FDG‐PET textural features in non‐small cell lung cancer. Medical Physics. 48(3). 1226–1238. 17 indexed citations
12.
Jauw, Yvonne W. S., Frederike Bensch, Adrienne H. Brouwers, et al.. (2019). Interobserver reproducibility of tumor uptake quantification with 89Zr-immuno-PET: a multicenter analysis. European Journal of Nuclear Medicine and Molecular Imaging. 46(9). 1840–1849. 9 indexed citations
13.
Kaalep, Andres, Coreline N. Burggraaff, Simone Pieplenbosch, et al.. (2019). Quantitative implications of the updated EARL 2019 PET–CT performance standards. EJNMMI Physics. 6(1). 39 indexed citations
14.
Jauw, Yvonne W. S., Otto S. Hoekstra, Emma R. Mulder, et al.. (2016). Inter-observer agreement for tumor uptake quantification of 89Zr-labeled anti-CD20 antibodies with PET. 57. 1412–1412. 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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