D. G. Kieback

1.4k total citations
31 papers, 946 citations indexed

About

D. G. Kieback is a scholar working on Oncology, Genetics and Cancer Research. According to data from OpenAlex, D. G. Kieback has authored 31 papers receiving a total of 946 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Oncology, 15 papers in Genetics and 12 papers in Cancer Research. Recurrent topics in D. G. Kieback's work include Estrogen and related hormone effects (15 papers), Breast Cancer Treatment Studies (10 papers) and HER2/EGFR in Cancer Research (7 papers). D. G. Kieback is often cited by papers focused on Estrogen and related hormone effects (15 papers), Breast Cancer Treatment Studies (10 papers) and HER2/EGFR in Cancer Research (7 papers). D. G. Kieback collaborates with scholars based in Germany, United Kingdom and Netherlands. D. G. Kieback's co-authors include Annette Hasenburg, Peyman Hadji, Daniel Rea, John M.S. Bartlett, Caroline Seynaeve, Christos Markopoulos, Hein Putter, E.T.M. Hille, Cornelis J.�H. van de Velde and Luc Dirix and has published in prestigious journals such as The Lancet, Journal of Clinical Oncology and JNCI Journal of the National Cancer Institute.

In The Last Decade

D. G. Kieback

31 papers receiving 932 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
D. G. Kieback Germany 14 530 393 372 248 221 31 946
Mauro Porpiglia Italy 11 456 0.9× 449 1.1× 564 1.5× 124 0.5× 97 0.4× 27 813
J. Bonneterre France 9 764 1.4× 464 1.2× 513 1.4× 462 1.9× 224 1.0× 28 1.3k
P. Neven Belgium 14 531 1.0× 222 0.6× 189 0.5× 192 0.8× 184 0.8× 24 836
Mustafa Al-Mubarak Canada 7 747 1.4× 397 1.0× 158 0.4× 302 1.2× 212 1.0× 13 1.1k
Lars Ottestad Norway 15 1.2k 2.3× 517 1.3× 583 1.6× 263 1.1× 488 2.2× 35 1.7k
Ritva Valavaara Finland 14 293 0.6× 223 0.6× 406 1.1× 134 0.5× 126 0.6× 29 900
Catherine Harper‐Wynne United Kingdom 13 549 1.0× 360 0.9× 381 1.0× 264 1.1× 183 0.8× 45 876
Nigel Sacks United Kingdom 18 521 1.0× 590 1.5× 466 1.3× 119 0.5× 268 1.2× 37 1.2k
G. Gademann Germany 5 442 0.8× 395 1.0× 517 1.4× 129 0.5× 61 0.3× 14 733
Davide Disalvatore Italy 19 684 1.3× 601 1.5× 124 0.3× 240 1.0× 266 1.2× 29 1.3k

Countries citing papers authored by D. G. Kieback

Since Specialization
Citations

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

Fields of papers citing papers by D. G. Kieback

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. G. Kieback. 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 D. G. Kieback. The network helps show where D. G. Kieback may publish in the future.

Co-authorship network of co-authors of D. G. Kieback

This figure shows the co-authorship network connecting the top 25 collaborators of D. G. Kieback. A scholar is included among the top collaborators of D. G. Kieback 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 D. G. Kieback. D. G. Kieback 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
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Bayani, Jane, Elizabeth Kornaga, Gun Ho Jang, et al.. (2017). Copy-number and targeted sequencing analyses to identify distinct prognostic groups: Implications for patient selection to targeted therapies amongst anti-endocrine therapy resistant early breast cancers.. Journal of Clinical Oncology. 35(15_suppl). 524–524. 1 indexed citations
3.
Bayani, Jane, Cindy Q. Yao, Fu Yan, et al.. (2017). Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine. npj Breast Cancer. 3(1). 3–3. 16 indexed citations
4.
Sabine, Vicky S., Cassandra Brookes, Camilla Drake, et al.. (2014). Mutational Analysis of PI3K/AKT Signaling Pathway in Tamoxifen Exemestane Adjuvant Multinational Pathology Study. Journal of Clinical Oncology. 32(27). 2951–2958. 86 indexed citations
5.
Bartlett, John M.S., Cassandra Brookes, Tammy Piper, et al.. (2013). Do type 1 receptor tyrosine kinases inform treatment choice? A prospectively planned analysis of the TEAM trial. British Journal of Cancer. 109(9). 2453–2461. 6 indexed citations
6.
Becker, Meike, et al.. (2012). Compliance, analgesic use and side-effect protection within a German cohort of the TEAM trial.. PubMed. 32(9). 3933–8. 4 indexed citations
7.
Christiansen, Jason, John M.S. Bartlett, Mark Gustavson, et al.. (2012). Validation of IHC4 algorithms for prediction of risk of recurrence in early breast cancer using both conventional and quantitative IHC approaches.. Journal of Clinical Oncology. 30(15_suppl). 517–517. 7 indexed citations
8.
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Bartlett, John M.S., Cassandra Brookes, Tammy Robson, et al.. (2011). Estrogen Receptor and Progesterone Receptor As Predictive Biomarkers of Response to Endocrine Therapy: A Prospectively Powered Pathology Study in the Tamoxifen and Exemestane Adjuvant Multinational Trial. Journal of Clinical Oncology. 29(12). 1531–1538. 142 indexed citations
10.
Velde, Cornelis JH van de, Daniel Rea, Caroline Seynaeve, et al.. (2011). Adjuvant tamoxifen and exemestane in early breast cancer (TEAM): a randomised phase 3 trial. The Lancet. 377(9762). 321–331. 264 indexed citations
11.
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Bartlett, JMS, Cassandra Brookes, Lucinda Billingham, et al.. (2009). A prospectively planned pathology study within the TEAM trial confirms that progesterone receptor expression is prognostic but is not predictive for differential response to exemestane versus tamoxifen.. Cancer Research. 69(2_Supplement). 81–81. 5 indexed citations
13.
Hadji, Peyman, M. Ziller, D. G. Kieback, et al.. (2009). The effect of exemestane or tamoxifen on markers of bone turnover: Results of a German sub-study of the Tamoxifen Exemestane Adjuvant Multicentre (TEAM) trial. The Breast. 18(3). 159–164. 42 indexed citations
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IJland, Marga M., et al.. (2005). Midline intravaginal slingplasty for treatment of urinary stress incontinence: results of an independent audit up to 2 years after surgery. International Urogynecology Journal. 16(6). 447–454. 6 indexed citations
16.
Oehler, Martin K., et al.. (2004). Functional characterization of somatic point mutations of the human estrogen receptor α (hERα) in adenomyosis uteri. Molecular Human Reproduction. 10(12). 853–860. 17 indexed citations
17.
Hasenburg, Annette, et al.. (2002). Evaluation of Patients after Extraperitoneal Lymph Node Dissection and Subsequent Radiotherapy for Cervical Cancer. Gynecologic Oncology. 84(2). 321–326. 34 indexed citations
18.
Oberhoff, C., D. G. Kieback, Rachel Würstlein, et al.. (2001). Topotecan Chemotherapy in Patients with Breast Cancer and Brain Metastases: Results of a Pilot Study. Oncology Research and Treatment. 24(3). 256–260. 81 indexed citations
19.
Kohlberger, Petra, Josefine Stani, G. Gitsch, D. G. Kieback, & G. Breitenecker. (1999). Comparative Evaluation of Seven Cell Collection Devices for Cervical Smears. Acta Cytologica. 43(6). 1023–1026. 17 indexed citations
20.
Beller, Fritz Karl, et al.. (1991). [Sexual development: development of secondary sex characteristics--Tanner stages 25 years later].. PubMed. 113(9). 499–509. 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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