Kevin ten Haaf

8.0k total citations · 3 hit papers
59 papers, 3.2k citations indexed

About

Kevin ten Haaf is a scholar working on Pulmonary and Respiratory Medicine, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Kevin ten Haaf has authored 59 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Pulmonary and Respiratory Medicine, 36 papers in Oncology and 11 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Kevin ten Haaf's work include Lung Cancer Diagnosis and Treatment (50 papers), Global Cancer Incidence and Screening (35 papers) and Lung Cancer Treatments and Mutations (29 papers). Kevin ten Haaf is often cited by papers focused on Lung Cancer Diagnosis and Treatment (50 papers), Global Cancer Incidence and Screening (35 papers) and Lung Cancer Treatments and Mutations (29 papers). Kevin ten Haaf collaborates with scholars based in Netherlands, United States and Canada. Kevin ten Haaf's co-authors include Harry J. de Koning, Matthijs Oudkerk, Carlijn M. van der Aalst, Rafael Meza, Jihyoun Jeon, Summer S. Han, Chung Yin Kong, Martin C. Tammemägi, Sylvia K. Plevritis and Eric J. Feuer and has published in prestigious journals such as JAMA, Annals of Internal Medicine and PLoS ONE.

In The Last Decade

Kevin ten Haaf

56 papers receiving 3.1k 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.

Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening

2014 351 citations Joost van Rosmalen, Marjolein A. Heuvelmans et al. profile →
Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies: A Comparative Modeling Study for the U.S. Preventive Services Task Force

2013 348 citations Harry J. de Koning, Rafael Meza et al. Annals of Internal Medicine profile →
Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography

2021 199 citations Rafael Meza, Jihyoun Jeon et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kevin ten Haaf Netherlands	28	2.6k	1.2k	1.1k	346	179	59	3.2k
Juan P. Wisnivesky United States	32	2.4k 0.9×	675 0.6×	482 0.4×	484 1.4×	152 0.8×	82	3.0k
Daniel R. Gomez United States	33	2.3k 0.9×	719 0.6×	917 0.8×	369 1.1×	345 1.9×	118	2.9k
Franz M.N.H. Schramel Netherlands	25	2.3k 0.9×	1.3k 1.1×	302 0.3×	449 1.3×	132 0.7×	110	2.9k
Shauna L. Hillman United States	24	2.0k 0.8×	1.1k 1.0×	735 0.7×	196 0.6×	86 0.5×	57	2.8k
Abigail T. Berman United States	28	1.2k 0.5×	720 0.6×	473 0.4×	230 0.7×	91 0.5×	102	1.9k
Sara Ramella Italy	22	1.7k 0.7×	648 0.6×	937 0.8×	275 0.8×	161 0.9×	136	2.4k
John A. Howington United States	21	2.2k 0.8×	648 0.6×	472 0.4×	749 2.2×	130 0.7×	50	2.8k
Christophe Dooms Belgium	36	3.7k 1.4×	1.1k 1.0×	1.1k 1.0×	782 2.3×	229 1.3×	142	4.6k
Abraham J. Wu United States	33	2.4k 0.9×	1.7k 1.5×	971 0.9×	1.2k 3.5×	253 1.4×	234	3.8k
Pehr Lind Sweden	27	1.4k 0.6×	592 0.5×	1.2k 1.1×	423 1.2×	180 1.0×	61	2.8k

Countries citing papers authored by Kevin ten Haaf

Since Specialization

Citations

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

Fields of papers citing papers by Kevin ten Haaf

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin ten Haaf. 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 Kevin ten Haaf. The network helps show where Kevin ten Haaf may publish in the future.

Co-authorship network of co-authors of Kevin ten Haaf

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin ten Haaf. A scholar is included among the top collaborators of Kevin ten Haaf 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 Kevin ten Haaf. Kevin ten Haaf 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

Bosch, I. van den, et al.. (2025). Impact of recruitment method on informed decision-making in lung cancer screening in the 4-IN-THE-LUNG-RUN trial. Lung Cancer. 207. 108686–108686. 1 indexed citations

Ding, Victoria Y., Kevin ten Haaf, Julie Wu, et al.. (2025). Eligibility and Prognostic Performance of Smoking Duration–Based Versus Pack-Year–Based U.S. National Lung Cancer Screening Criteria Across Racial and Ethnic Groups. Annals of Internal Medicine. 179(2). 196–206.

Koning, Harry J. de, et al.. (2024). Medical costs of lung cancer by stage, histology and first-line treatment modality in the Netherlands (2012–2021). European Journal of Cancer. 208. 114231–114231. 6 indexed citations

Haaf, Kevin ten, Pianpian Cao, Jean Hai Ein Yong, et al.. (2024). The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations. Medical Decision Making. 44(5). 497–511. 2 indexed citations

Meza, Rafael, Pianpian Cao, Jihyoun Jeon, et al.. (2023). Assessing the impact of increasing lung screening eligibility by relaxing the maximum years‐since‐quit threshold: A simulation modeling study. Cancer. 130(2). 244–255. 4 indexed citations

Tomonaga, Yuki, et al.. (2023). Cost‐effectiveness of risk‐based low‐dose computed tomography screening for lung cancer in Switzerland. International Journal of Cancer. 154(4). 636–647. 7 indexed citations

Aredo, Jacqueline V., Eunji Choi, Victoria Y. Ding, et al.. (2022). Racial and Ethnic Disparities in Lung Cancer Screening by the 2021 USPSTF Guidelines Versus Risk-Based Criteria: The Multiethnic Cohort Study. JNCI Cancer Spectrum. 6(3). 23 indexed citations

Haaf, Kevin ten, et al.. (2021). Cost-effectiveness analysis of breast cancer screening using mammography in singapore:A modeling study. EUR Research Repository (Erasmus University Rotterdam). 13 indexed citations

Haaf, Kevin ten, Carlijn M. van der Aalst, Harry J. de Koning, Rudolf Kaaks, & Martin C. Tammemägi. (2021). Personalising lung cancer screening: An overview of risk‐stratification opportunities and challenges. International Journal of Cancer. 149(2). 250–263. 46 indexed citations

10.

Blom, Erik F., Kevin ten Haaf, & Harry J. de Koning. (2020). Systematic Review and Meta-Analysis of Community- and Choice-Based Health State Utility Values for Lung Cancer. PharmacoEconomics. 38(11). 1187–1200. 21 indexed citations

11.

Han, Summer S., Eric K.H. Chow, Kevin ten Haaf, et al.. (2019). P1.11-03 Disparities and National Lung Cancer Screening Guidelines in the U.S. Population. Journal of Thoracic Oncology. 14(10). S515–S516. 1 indexed citations

12.

Koning, Harry J. de, Carlijn van der Aalst, Kevin ten Haaf, & Matthijs Oudkerk. (2018). PL02.05 Effects of Volume CT Lung Cancer Screening: Mortality Results of the NELSON Randomised-Controlled Population Based Trial. Journal of Thoracic Oncology. 13(10). S185–S185. 154 indexed citations

13.

Walter, Joan, Marjolein A. Heuvelmans, Kevin ten Haaf, et al.. (2018). Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study. Thorax. 74(3). 247–253. 18 indexed citations

14.

Han, Summer S., Kevin ten Haaf, William D. Hazelton, et al.. (2017). The impact of overdiagnosis on the selection of efficient lung cancer screening strategies. International Journal of Cancer. 140(11). 2436–2443. 33 indexed citations

15.

Haaf, Kevin ten, Martin C. Tammemägi, Susan J. Bondy, et al.. (2017). Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada. PLoS Medicine. 14(2). e1002225–e1002225. 96 indexed citations

16.

Haaf, Kevin ten, Jihyoun Jeon, Martin C. Tammemägi, et al.. (2017). Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study. PLoS Medicine. 14(4). e1002277–e1002277. 176 indexed citations

17.

Verbeek, André L. M., et al.. (2016). Extrapolation of pre-screening trends: Impact of assumptions on overdiagnosis estimates by mammographic screening. Cancer Epidemiology. 42. 147–153. 6 indexed citations

18.

Haaf, Kevin ten & Harry J. de Koning. (2015). Should Never-Smokers at Increased Risk for Lung Cancer Be Screened?. Journal of Thoracic Oncology. 10(9). 1285–1291. 24 indexed citations

19.

Haaf, Kevin ten, Joost van Rosmalen, & Harry J. de Koning. (2014). Lung Cancer Detectability by Test, Histology, Stage, and Gender: Estimates from the NLST and the PLCO Trials. Cancer Epidemiology Biomarkers & Prevention. 24(1). 154–161. 81 indexed citations

20.

Koning, Harry J. de, Rafael Meza, Sylvia K. Plevritis, et al.. (2013). Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies: A Comparative Modeling Study for the U.S. Preventive Services Task Force. Annals of Internal Medicine. 160(5). 311–320. 348 indexed citations breakdown →

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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