Ed Schuuring

16.1k total citations · 1 hit paper
240 papers, 8.4k citations indexed

About

Ed Schuuring is a scholar working on Oncology, Cancer Research and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ed Schuuring has authored 240 papers receiving a total of 8.4k indexed citations (citations by other indexed papers that have themselves been cited), including 101 papers in Oncology, 85 papers in Cancer Research and 77 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ed Schuuring's work include Lung Cancer Treatments and Mutations (64 papers), Cancer Genomics and Diagnostics (63 papers) and Lymphoma Diagnosis and Treatment (37 papers). Ed Schuuring is often cited by papers focused on Lung Cancer Treatments and Mutations (64 papers), Cancer Genomics and Diagnostics (63 papers) and Lymphoma Diagnosis and Treatment (37 papers). Ed Schuuring collaborates with scholars based in Netherlands, Belgium and Germany. Ed Schuuring's co-authors include Philip M. Kluin, G. Bea A. Wisman, Katja Philippo, Daphne de Jong, Ate G.J. van der Zee, Gustaaf W. van Imhoff, Evert-Jan G. Boerma, Hanneke C. Kluin‐Nelemans, Harry J.M. Groen and Geertruida H. de Bock and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Ed Schuuring

236 papers receiving 8.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Double-hit B-cell lymphomas

2010 451 citations Ed Schuuring, Philip M. Kluin et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ed Schuuring Netherlands	49	3.3k	3.0k	2.6k	2.2k	1.5k	240	8.4k
Laura E. MacConaill United States	44	2.7k 0.8×	3.6k 1.2×	1.3k 0.5×	1.7k 0.8×	1.6k 1.1×	91	8.0k
Laurence Brugières France	52	3.1k 0.9×	2.9k 1.0×	3.2k 1.3×	1.7k 0.8×	1.8k 1.2×	239	9.7k
Raymond R. Tubbs United States	59	4.7k 1.4×	2.7k 0.9×	2.6k 1.0×	1.4k 0.7×	2.3k 1.5×	276	10.8k
Peter Vandenberghe Belgium	58	2.2k 0.7×	3.1k 1.0×	2.4k 0.9×	1.0k 0.5×	1.5k 1.0×	229	10.7k
Ruth L. Katz United States	46	3.5k 1.1×	3.3k 1.1×	2.0k 0.8×	2.1k 0.9×	2.1k 1.4×	212	9.3k
Francesco Pezzella United Kingdom	58	4.1k 1.3×	7.0k 2.4×	1.5k 0.6×	4.5k 2.0×	1.9k 1.2×	159	12.1k
Stephan Dirnhofer Switzerland	52	3.0k 0.9×	2.9k 1.0×	2.5k 1.0×	949 0.4×	1.2k 0.8×	176	8.6k
Thomas M. Grogan United States	52	5.0k 1.5×	3.1k 1.1×	3.4k 1.3×	831 0.4×	996 0.7×	160	9.2k
Marina N. Nikiforova United States	67	5.6k 1.7×	5.4k 1.8×	3.0k 1.2×	3.8k 1.7×	2.3k 1.5×	209	18.8k
Laurence Lamant France	45	3.3k 1.0×	1.9k 0.7×	4.4k 1.7×	681 0.3×	1.5k 1.0×	203	7.9k

Countries citing papers authored by Ed Schuuring

Since Specialization

Citations

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

Fields of papers citing papers by Ed Schuuring

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ed Schuuring

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

All Works

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20 of 20 papers shown

Leest, Paul van der, Idris Bahce, D. Michiel Pegtel, et al.. (2025). Cell-Free DNA Based Next-Generation Sequencing Does Not Differentiate Between Oligoprogression and Systemic Progression in Non-Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors—An Explorative Study. International Journal of Molecular Sciences. 26(16). 8087–8087.

Bhattacharya, Arkajyoti, Marieke T. de Boer, Bettien M. van Hemel, et al.. (2023). Identification of a methylation panel as an alternative triage to detect CIN3+ in hrHPV-positive self-samples from the population-based cervical cancer screening programme. Clinical Epigenetics. 15(1). 103–103. 7 indexed citations

Tamminga, Menno, Gerton Lunter, Léon C.L.T. van Kempen, et al.. (2023). Using genomic scars to select immunotherapy beneficiaries in advanced non-small cell lung cancer. Scientific Reports. 13(1). 6581–6581. 3 indexed citations

Kuijpers, Chantal C. H. J., Harry J.M. Groen, Wim Timens, et al.. (2022). Detection of NTRK Fusions and TRK Expression and Performance of pan-TRK Immunohistochemistry in Routine Diagnostics: Results from a Nationwide Community-Based Cohort. Diagnostics. 12(3). 668–668. 18 indexed citations

Lamy, Pierre‐Jean, Paul van der Leest, Catherine Becht, et al.. (2020). Mass Spectrometry as a Highly Sensitive Method for Specific Circulating Tumor DNA Analysis in NSCLC: A Comparison Study. Cancers. 12(10). 3002–3002. 23 indexed citations

Tamminga, Menno, T. Jeroen N. Hiltermann, Ed Schuuring, et al.. (2020). Immune microenvironment composition in non‐small cell lung cancer and its association with survival. Clinical & Translational Immunology. 9(6). e1142–e1142. 126 indexed citations

Melchers, Lieuwe J., Ed Schuuring, Jan L.N. Roodenburg, et al.. (2019). Addition of tumour infiltration depth and extranodal extension improves the prognostic value of the pathological TNM classification for early‐stage oral squamous cell carcinoma. Histopathology. 75(3). 329–337. 19 indexed citations

Keppens, Cleo, et al.. (2019). Variation in nomenclature of somatic variants for selection of oncological therapies: Can we reach a consensus soon?. Human Mutation. 41(1). 7–16. 1 indexed citations

Bock, Geertruida H. de, Maartje Noordhuis, Lorian Slagter‐Menkema, et al.. (2019). Standardised Ki‐67 proliferation index assessment in early‐stage laryngeal squamous cell carcinoma in relation to local control and survival after primary radiotherapy. Clinical Otolaryngology. 45(1). 12–20. 4 indexed citations

10.

Wekken, Anthonie J. van der, M F Mastik, Lizza Hendriks, et al.. (2017). Dichotomous ALK-IHC Is a Better Predictor for ALK Inhibition Outcome than Traditional ALK-FISH in Advanced Non–Small Cell Lung Cancer. Clinical Cancer Research. 23(15). 4251–4258. 51 indexed citations

11.

Noordhuis, Maartje, Klaske A. ten Hoor, Lorian Slagter‐Menkema, et al.. (2012). The role of ATM and 53BP1 as predictive markers in cervical cancer. International Journal of Cancer. 131(9). 2056–2066. 33 indexed citations

12.

Noordhuis, Maartje, Rudolf S.N. Fehrmann, G. Bea A. Wisman, et al.. (2011). Involvement of the TGF-β and β-Catenin Pathways in Pelvic Lymph Node Metastasis in Early-Stage Cervical Cancer. Clinical Cancer Research. 17(6). 1317–1330. 105 indexed citations

13.

Jong, Monique C. de, Jimmy Pramana, Jacqueline E. van der Wal, et al.. (2010). CD44 Expression Predicts Local Recurrence after Radiotherapy in Larynx Cancer. Clinical Cancer Research. 16(21). 5329–5338. 135 indexed citations

14.

Noordhuis, Maartje, Jasper J.H. Eijsink, Klaske A. ten Hoor, et al.. (2009). Expression of Epidermal Growth Factor Receptor (EGFR) and Activated EGFR Predict Poor Response to (Chemo)radiation and Survival in Cervical Cancer. Clinical Cancer Research. 15(23). 7389–7397. 92 indexed citations

15.

Yang, Nan, Jasper J.H. Eijsink, Ágnes Lendvai, et al.. (2009). Methylation Markers for CCNA1 and C13ORF18 Are Strongly Associated with High-Grade Cervical Intraepithelial Neoplasia and Cervical Cancer in Cervical Scrapings. Cancer Epidemiology Biomarkers & Prevention. 18(11). 3000–3007. 55 indexed citations

16.

Hoque, Mohammad Obaidul, Myoung Sook Kim, Kimberly Laskie Ostrow, et al.. (2008). Genome-Wide Promoter Analysis Uncovers Portions of the Cancer Methylome. Cancer Research. 68(8). 2661–2670. 115 indexed citations

17.

Booman, Marije, Annuska M. Glas, Sietske A. Riemersma, et al.. (2006). Mechanisms and Effects of Loss of Human Leukocyte Antigen Class II Expression in Immune-Privileged Site-Associated B-Cell Lymphoma. Clinical Cancer Research. 12(9). 2698–2705. 53 indexed citations

18.

Nijhuis, Esther R., Nathalie Reesink‐Peters, G. Bea A. Wisman, et al.. (2006). An Overview of Innovative Techniques to Improve Cervical Cancer Screening. Analytical Cellular Pathology. 28(5-6). 233–246. 14 indexed citations

19.

Riemersma, Sietske A., Joost J. Oudejans, Enno Dreef, et al.. (2005). High numbers of tumour‐infiltrating activated cytotoxic T lymphocytes, and frequent loss of HLA class I and II expression, are features of aggressive B cell lymphomas of the brain and testis. The Journal of Pathology. 206(3). 328–336. 48 indexed citations

20.

Reesink‐Peters, Nathalie, Marco N. Helder, G. Bea A. Wisman, et al.. (2002). Detection of telomerase, its components, and human papillomavirus in cervical scrapings as a tool for triage in women with cervical dysplasia. Journal of Clinical Pathology. 56(1). 31–35. 32 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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