Carmen van Dooijeweert

517 total citations
29 papers, 304 citations indexed

About

Carmen van Dooijeweert is a scholar working on Oncology, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Carmen van Dooijeweert has authored 29 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Oncology, 11 papers in Artificial Intelligence and 10 papers in Cancer Research. Recurrent topics in Carmen van Dooijeweert's work include AI in cancer detection (11 papers), Breast Cancer Treatment Studies (10 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Carmen van Dooijeweert is often cited by papers focused on AI in cancer detection (11 papers), Breast Cancer Treatment Studies (10 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Carmen van Dooijeweert collaborates with scholars based in Netherlands, Germany and United Kingdom. Carmen van Dooijeweert's co-authors include P. J. van Diest, Elsken van der Wall, Ivette A.G. Deckers, Ian O. Ellis, Stefan M. Willems, Lucy Overbeek, Britt B.M. Suelmann, Chantal C. H. J. Kuijpers, Inge O. Baas and Sabine C. Linn and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and International Journal of Cancer.

In The Last Decade

Carmen van Dooijeweert

26 papers receiving 299 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Carmen van Dooijeweert Netherlands	11	131	116	105	99	65	29	304
Jionghui Gu China	10	83 0.6×	124 1.1×	56 0.5×	217 2.2×	40 0.6×	22	368
Saba Shafi United States	10	135 1.0×	123 1.1×	49 0.5×	126 1.3×	50 0.8×	42	387
Mengsu Xiao China	12	78 0.6×	171 1.5×	80 0.8×	203 2.1×	56 0.9×	39	390
Birgid Schömig‐Markiefka Germany	7	66 0.5×	84 0.7×	54 0.5×	87 0.9×	22 0.3×	17	226
Michael Lippert Denmark	7	109 0.8×	122 1.1×	101 1.0×	108 1.1×	24 0.4×	9	300
Christina Glasner Germany	3	93 0.7×	129 1.1×	53 0.5×	153 1.5×	62 1.0×	4	279
Emmanuel Agosto‐Arroyo United States	5	81 0.6×	207 1.8×	57 0.5×	148 1.5×	39 0.6×	11	327
Ann-Christin Woerl Germany	4	134 1.0×	183 1.6×	73 0.7×	219 2.2×	92 1.4×	6	392
Jieun Koh South Korea	13	64 0.5×	70 0.6×	64 0.6×	189 1.9×	68 1.0×	25	394
Aurélie Fernandez Germany	5	119 0.9×	151 1.3×	64 0.6×	181 1.8×	85 1.3×	8	353

Countries citing papers authored by Carmen van Dooijeweert

Since Specialization

Citations

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

Fields of papers citing papers by Carmen van Dooijeweert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carmen van Dooijeweert. 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 Carmen van Dooijeweert. The network helps show where Carmen van Dooijeweert may publish in the future.

Co-authorship network of co-authors of Carmen van Dooijeweert

This figure shows the co-authorship network connecting the top 25 collaborators of Carmen van Dooijeweert. A scholar is included among the top collaborators of Carmen van Dooijeweert 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 Carmen van Dooijeweert. Carmen van Dooijeweert 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

Gerritsen, W.J., Tri Q. Nguyen, Carmen van Dooijeweert, et al.. (2025). Monitoring Immunohistochemical Staining Variations Using Artificial Intelligence on Standardized Controls. Laboratory Investigation. 105(5). 104105–104105. 1 indexed citations

Preković, Stefan, Britt B.M. Suelmann, Janneke Verloop, et al.. (2025). Worse prognosis for breast cancer diagnosed in advanced pregnancy and shortly postpartum: an update of the Dutch pregnancy-associated breast cancer cohort. Breast Cancer Research and Treatment. 214(2). 191–204. 1 indexed citations

Samuels, Martin, Natalie D. ter Hoeve, Nikolas Stathonikos, et al.. (2025). Head-to-Head Comparison of 2 Artificial Intelligence Tools for Detecting Lymph Node Metastases in Whole-Slide Pathology Images Within and Beyond Their Intended Use. Modern Pathology. 38(12). 100905–100905.

Dooijeweert, Carmen van, Tri Q. Nguyen, Michael Lynch, et al.. (2025). Prospective Clinical Implementation of Paige Prostate Detect Artificial Intelligence Assistance in the Detection of Prostate Cancer in Prostate Biopsies: CONFIDENT P Trial Implementation of Artificial Intelligence Assistance in Prostate Cancer Detection. JCO Clinical Cancer Informatics. 9(9). e2400193–e2400193. 2 indexed citations

Dooijeweert, Carmen van, Natalie D. ter Hoeve, Celien P.H. Vreuls, et al.. (2024). Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial. Nature Cancer. 5(8). 1195–1205. 13 indexed citations

Stathonikos, Nikolas, et al.. (2023). The CONFIDENT-P trial: Clinical implementation of artificial intelligence assistance in prostate cancer pathology.. Journal of Clinical Oncology. 41(6_suppl). TPS405–TPS405.

Suelmann, Britt B.M., Carmen van Dooijeweert, Janneke Verloop, et al.. (2022). Prognosis of pregnancy-associated breast cancer: inferior outcome in patients diagnosed during second and third gestational trimesters and lactation. Breast Cancer Research and Treatment. 192(1). 175–189. 7 indexed citations

Fransen, Nina L., Andreas F.‐P. Sonnen, Tri Q. Nguyen, et al.. (2022). Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective. Diagnostics. 12(5). 1042–1042. 12 indexed citations

Dooijeweert, Carmen van, Katja K.H. Aben, Britt B.M. Suelmann, et al.. (2022). Interlaboratory Gleason grading variation affects treatment: a Dutch historic cohort study in 30 509 patients with prostate cancer. Journal of Clinical Pathology. 76(10). 690–697. 3 indexed citations

10.

Dooijeweert, Carmen van, et al.. (2022). Nationwide differences in cytology fixation and processing methods and their impact on interlaboratory variation in PD-L1 positivity. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 482(4). 707–720. 8 indexed citations

11.

Suelmann, Britt B.M., et al.. (2022). Genomic copy number alterations as biomarkers for triple negative pregnancy-associated breast cancer. Cellular Oncology. 45(4). 591–600. 2 indexed citations

12.

Suelmann, Britt B.M., Carmen van Dooijeweert, Elsken van der Wall, Sabine C. Linn, & P. J. van Diest. (2021). Pregnancy-associated breast cancer: nationwide Dutch study confirms a discriminatory aggressive histopathologic profile. Breast Cancer Research and Treatment. 186(3). 699–704. 25 indexed citations

13.

Dooijeweert, Carmen van, Inge O. Baas, Ivette A.G. Deckers, et al.. (2021). The increasing importance of histologic grading in tailoring adjuvant systemic therapy in 30,843 breast cancer patients. Breast Cancer Research and Treatment. 187(2). 577–586. 5 indexed citations

14.

Voorham, Quirinus J.M., et al.. (2021). Considerable interlaboratory variation in PD-L1 positivity in a nationwide cohort of non-small cell lung cancer patients. Lung Cancer. 159. 117–126. 6 indexed citations

15.

Dooijeweert, Carmen van, P. J. van Diest, & Ian O. Ellis. (2021). Grading of invasive breast carcinoma: the way forward. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 480(1). 33–43. 46 indexed citations

16.

Suelmann, Britt B.M., et al.. (2021). Receptor status of breast cancer diagnosed during pregnancy: A literature review. Critical Reviews in Oncology/Hematology. 168. 103494–103494. 5 indexed citations

17.

Dooijeweert, Carmen van, P. J. van Diest, Inge O. Baas, Elsken van der Wall, & Ivette A.G. Deckers. (2020). Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports. Diagnostic Pathology. 15(1). 52–52. 7 indexed citations

18.

Dooijeweert, Carmen van, Ivette A.G. Deckers, Inge O. Baas, Elsken van der Wall, & P. J. van Diest. (2019). Hormone- and HER2-receptor assessment in 33,046 breast cancer patients: a nationwide comparison of positivity rates between pathology laboratories in the Netherlands. Breast Cancer Research and Treatment. 175(2). 487–497. 19 indexed citations

19.

Dooijeweert, Carmen van, P. J. van Diest, Stefan M. Willems, et al.. (2019). Significant inter‐ and intra‐laboratory variation in grading of invasive breast cancer: A nationwide study of 33,043 patients in the Netherlands. International Journal of Cancer. 146(3). 769–780. 44 indexed citations

20.

Dooijeweert, Carmen van, P. J. van Diest, Stefan M. Willems, et al.. (2018). Significant inter- and intra-laboratory variation in grading of ductal carcinoma in situ of the breast: a nationwide study of 4901 patients in the Netherlands. Breast Cancer Research and Treatment. 174(2). 479–488. 26 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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