Arjan Diepstra

7.1k total citations
161 papers, 3.6k citations indexed

About

Arjan Diepstra is a scholar working on Pathology and Forensic Medicine, Oncology and Immunology. According to data from OpenAlex, Arjan Diepstra has authored 161 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 103 papers in Pathology and Forensic Medicine, 60 papers in Oncology and 54 papers in Immunology. Recurrent topics in Arjan Diepstra's work include Lymphoma Diagnosis and Treatment (100 papers), Immune Cell Function and Interaction (37 papers) and Viral-associated cancers and disorders (34 papers). Arjan Diepstra is often cited by papers focused on Lymphoma Diagnosis and Treatment (100 papers), Immune Cell Function and Interaction (37 papers) and Viral-associated cancers and disorders (34 papers). Arjan Diepstra collaborates with scholars based in Netherlands, United States and United Kingdom. Arjan Diepstra's co-authors include Anke van den Berg, Lydia Visser, Sibrand Poppema, Joost Kluiver, Ewerton Marques Maggio, Ilja M. Nolte, Debora de Jong, Marijke Niens, S Poppema and Gustaaf W. van Imhoff and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Lancet and Journal of Clinical Oncology.

In The Last Decade

Arjan Diepstra

155 papers receiving 3.6k citations

Peers

Arjan Diepstra
Arjan Diepstra
Citations per year, relative to Arjan Diepstra Arjan Diepstra (= 1×) peers Przemysław Juszczyński

Countries citing papers authored by Arjan Diepstra

Since Specialization
Citations

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

Fields of papers citing papers by Arjan Diepstra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arjan Diepstra

This figure shows the co-authorship network connecting the top 25 collaborators of Arjan Diepstra. A scholar is included among the top collaborators of Arjan Diepstra 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 Arjan Diepstra. Arjan Diepstra 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.
Zhong, Yujie, Martijn Terpstra, Marcel Nijland, et al.. (2025). Molecular profiling of cell-free DNA from classic Hodgkin lymphoma patients identifies potential prognostic clusters and corresponds with disease dynamics. Annals of Hematology. 104(3). 1789–1800. 1 indexed citations
2.
Kemps, Paul G., Lennart Kester, Marijn A. Vermeulen, et al.. (2024). Demographics and additional haematologic cancers of patients with histiocytic/dendritic cell neoplasms. Histopathology. 84(5). 837–846. 4 indexed citations
3.
Brink, Mirian, Marjolein van der Poel, Marie José Kersten, et al.. (2024). Treatment strategies and outcome in relapsed peripheral T-cell lymphoma: results from the Netherlands Cancer Registry. Blood Advances. 8(14). 3619–3628. 7 indexed citations
4.
Bröckelmann, Paul J., Lydia Visser, Conrad‐Amadeus Voltin, et al.. (2024). Serum TARC Dynamics Correlate with Clinical Response and Metabolic Tumor Volume during Anti-PD1-Based First-Line HL Treatment in the GHSG Phase II Nivahl Trial. Blood. 144(Supplement 1). 3044–3044. 1 indexed citations
5.
Weinhäuser, Isabel, Diego A. Pereira‐Martins, Luciana Yamamoto Almeida, et al.. (2023). M2 macrophages drive leukemic transformation by imposing resistance to phagocytosis and improving mitochondrial metabolism. Science Advances. 9(15). eadf8522–eadf8522. 28 indexed citations
6.
Plaça, Jéssica Rodrigues, Arjan Diepstra, G. Tjitske Los-de Vries, et al.. (2022). Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma. Cancers. 14(5). 1346–1346. 1 indexed citations
7.
Ciriello, Giovanni, Nathalie Lailler, Elisa de Stanchina, et al.. (2022). Frequent 4EBP1 Amplification Induces Synthetic Dependence on FGFR Signaling in Cancer. Cancers. 14(10). 2397–2397. 3 indexed citations
8.
Plaça, Jéssica Rodrigues, Erlin A. Haacke, Martijn Terpstra, et al.. (2022). Low Mutational Burden of Extranodal Marginal Zone Lymphoma of Mucosa-Associated Lymphoid Tissue in Patients with Primary Sjogren’s Syndrome. Cancers. 14(4). 1010–1010. 9 indexed citations
9.
Schrader, Anne M.R., Ruben A. L. de Groen, Rein Willemze, et al.. (2022). Cell-of-origin classification using the Hans and Lymph2Cx algorithms in primary cutaneous large B-cell lymphomas. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 480(3). 667–675. 14 indexed citations
10.
Oldhoff, J, Arjan Diepstra, Peter Valent, et al.. (2021). Case report of a clinically indolent but morphologically high‐grade cutaneous mast cell tumor in an adult: Atypical cutaneous mastocytoma or mast cell sarcoma?. Journal of Cutaneous Pathology. 48(11). 1404–1409. 2 indexed citations
11.
Donk, Pim P. van de, Thijs T. Wind, Elly L. van der Veen, et al.. (2021). Interleukin-2 PET imaging in patients with metastatic melanoma before and during immune checkpoint inhibitor therapy. European Journal of Nuclear Medicine and Molecular Imaging. 48(13). 4369–4376. 35 indexed citations
12.
Khoei, Nazlisadat Seyed, Robert Carreras‐Torres, Neil Murphy, et al.. (2021). Genetically Raised Circulating Bilirubin Levels and Risk of Ten Cancers: A Mendelian Randomization Study. Cells. 10(2). 394–394. 22 indexed citations
13.
Jansen, Patty M., Hendrik Veelken, Maarten H. Vermeer, et al.. (2017). High Prevalence of Oncogenic MYD88 and CD79B Mutations in Intravascular Large B-Cell Lymphoma: Implication for Therapy with Bruton's Kinase Inhibitors?. Blood. 130. 4000–4000. 1 indexed citations
14.
Diepstra, Arjan, Debora de Jong, Jasper Koerts, et al.. (2016). MUTATIONS IN CD58 AND MYB IN HODGKIN LYMPHOMA. Haematologica. 101. 4–5. 1 indexed citations
15.
Veeger, Nic J.G.M., Francisca Ong, Wilma G.J.M. Smit, et al.. (2016). Progression of a solitary plasmacytoma to multiple myeloma. A population‐based registry of the northern Netherlands. British Journal of Haematology. 175(4). 661–667. 40 indexed citations
16.
Kushekhar, Kushi, Anke van den Berg, Ilja M. Nolte, et al.. (2014). Genetic Associations in Classical Hodgkin Lymphoma: A Systematic Review and Insights into Susceptibility Mechanisms. Cancer Epidemiology Biomarkers & Prevention. 23(12). 2737–2747. 34 indexed citations
17.
Niens, Marijke, Anke van den Berg, Arjan Diepstra, et al.. (2006). The Human Leukocyte Antigen Class I Region Is Associated with EBV-Positive Hodgkin's Lymphoma: HLA-A and HLA Complex Group 9 Are Putative Candidate Genes. Cancer Epidemiology Biomarkers & Prevention. 15(11). 2280–2284. 26 indexed citations
18.
Diepstra, Arjan, et al.. (2003). HLA-G expression in classical Hodgkin lymphoma.. Data Archiving and Networked Services (DANS). 1 indexed citations
19.
Diepstra, Arjan, et al.. (2002). Low frequency of FAS mutations in Reed-Sternberg cells of Hodgkin lymphoma.. Data Archiving and Networked Services (DANS). 2 indexed citations
20.
Diepstra, Arjan, et al.. (1997). The absence of effective T-cell activation in Hodgkin's disease may be the result of TGF beta production by Reed-Sternberg cells.. Blood. 90(10). 3938–3938. 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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