Ralf Kronenwett

9.6k total citations · 1 hit paper
131 papers, 5.1k citations indexed

About

Ralf Kronenwett is a scholar working on Cancer Research, Oncology and Molecular Biology. According to data from OpenAlex, Ralf Kronenwett has authored 131 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Cancer Research, 52 papers in Oncology and 40 papers in Molecular Biology. Recurrent topics in Ralf Kronenwett's work include Breast Cancer Treatment Studies (46 papers), Advanced Breast Cancer Therapies (26 papers) and HER2/EGFR in Cancer Research (22 papers). Ralf Kronenwett is often cited by papers focused on Breast Cancer Treatment Studies (46 papers), Advanced Breast Cancer Therapies (26 papers) and HER2/EGFR in Cancer Research (22 papers). Ralf Kronenwett collaborates with scholars based in Germany, United States and Austria. Ralf Kronenwett's co-authors include Rainer Haas, Carsten Denkert, Manfred Dietel, Berit Müller, Sibylle Loibl, Silvia Darb‐Esfahani, Gϋnter von Minckwitz, Wilko Weichert, Knut Engels and Christine Solbach and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Ralf Kronenwett

128 papers receiving 5.0k citations

Hit Papers

Tumor-Associated Lymphocy... 2009 2026 2014 2020 2009 400 800 1.2k
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.

  1. Tumor-Associated Lymphocytes As an Independent Predictor of Response to Neoadjuvant Chemotherapy in Breast Cancer
    2009 1.3k citations Carsten Denkert, Sibylle Loibl et al. Journal of Clinical Oncology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ralf Kronenwett Germany 36 2.6k 1.8k 1.5k 1.1k 831 131 5.1k
José Adélaı̈de France 47 2.7k 1.0× 1.9k 1.1× 4.4k 3.0× 658 0.6× 842 1.0× 130 7.6k
Jaco Kraan Netherlands 36 1.6k 0.6× 1.2k 0.7× 1.1k 0.7× 494 0.4× 432 0.5× 112 3.5k
Paul Waterhouse Canada 29 1.7k 0.7× 1.7k 0.9× 1.9k 1.3× 937 0.8× 379 0.5× 49 4.2k
Max Chaffanet France 36 1.5k 0.6× 1.0k 0.6× 2.4k 1.6× 377 0.3× 953 1.1× 105 4.4k
Dalong Qian United States 16 2.2k 0.8× 1.6k 0.9× 3.6k 2.4× 646 0.6× 524 0.6× 30 5.4k
Klaus Beiske Norway 35 1.2k 0.5× 691 0.4× 938 0.6× 1.3k 1.2× 588 0.7× 119 4.1k
Malek Faham United States 27 1.2k 0.5× 748 0.4× 1.5k 1.0× 706 0.6× 682 0.8× 92 4.2k
Michael Datto United States 34 2.2k 0.8× 1.4k 0.8× 4.1k 2.7× 367 0.3× 309 0.4× 69 6.4k
Otávia L. Caballero United States 38 3.1k 1.2× 1.4k 0.8× 4.9k 3.3× 2.8k 2.5× 258 0.3× 74 7.9k
Mark J. Routbort United States 41 1.7k 0.6× 1.8k 1.0× 1.6k 1.1× 270 0.2× 1.3k 1.6× 162 5.4k

Countries citing papers authored by Ralf Kronenwett

Since Specialization
Citations

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

Fields of papers citing papers by Ralf Kronenwett

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ralf Kronenwett

This figure shows the co-authorship network connecting the top 25 collaborators of Ralf Kronenwett. A scholar is included among the top collaborators of Ralf Kronenwett 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 Ralf Kronenwett. Ralf Kronenwett 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.
Jank, Paul, Judith Lindner, Annika Lehmann, et al.. (2021). Comparison of risk assessment in 1652 early ER positive, HER2 negative breast cancer in a real-world data set: classical pathological parameters vs. 12-gene molecular assay (EndoPredict). Breast Cancer Research and Treatment. 191(2). 327–333. 1 indexed citations
2.
Buus, Richard, Ivana Šestak, Ralf Kronenwett, et al.. (2020). Molecular Drivers of Oncotype DX, Prosigna, EndoPredict, and the Breast Cancer Index: A TransATAC Study. Journal of Clinical Oncology. 39(2). 126–135. 62 indexed citations
3.
Šestak, Ivana, Miguel Martín, Peter Dubsky, et al.. (2019). Prediction of chemotherapy benefit by EndoPredict in patients with breast cancer who received adjuvant endocrine therapy plus chemotherapy or endocrine therapy alone. Breast Cancer Research and Treatment. 176(2). 377–386. 57 indexed citations
4.
Loibl, Sibylle, Karsten E. Weber, Jens Huober, et al.. (2018). Risk Assessment after Neoadjuvant Chemotherapy in Luminal Breast Cancer Using a Clinicomolecular Predictor. Clinical Cancer Research. 24(14). 3358–3365. 8 indexed citations
5.
Šestak, Ivana, Richard Buus, Jack Cuzick, et al.. (2018). Comparison of the Performance of 6 Prognostic Signatures for Estrogen Receptor–Positive Breast Cancer. JAMA Oncology. 4(4). 545–545. 204 indexed citations
6.
Buus, Richard, Ivana Šestak, Ralf Kronenwett, et al.. (2016). Comparison of EndoPredict and EPclin With Oncotype DX Recurrence Score for Prediction of Risk of Distant Recurrence After Endocrine Therapy. JNCI Journal of the National Cancer Institute. 108(11). djw149–djw149. 152 indexed citations
7.
Linardou, Helena, Konstantine T. Kalogeras, Ralf Kronenwett, et al.. (2015). Prognostic Significance of VEGFC and VEGFR1 mRNA Expression According to HER2 Status in Breast Cancer: A Study of Primary Tumors from Patients with High-risk Early Breast Cancer Participating in a Randomized Hellenic Cooperative Oncology Group Trial.. PubMed. 35(7). 4023–36. 6 indexed citations
8.
Martín, Miguel, Jan C. Brase, Lourdes Calvo, et al.. (2014). Clinical validation of the EndoPredict test in node-positive, chemotherapy-treated ER+/HER2− breast cancer patients: results from the GEICAM 9906 trial. Breast Cancer Research. 16(2). R38–R38. 107 indexed citations
9.
Dubsky, Peter, Jan C. Brase, R. Jakesz, et al.. (2013). The EndoPredict score provides prognostic information on late distant metastases in ER+/HER2− breast cancer patients. British Journal of Cancer. 109(12). 2959–2964. 235 indexed citations
10.
11.
Denkert, Carsten, Sibylle Loibl, Ralf Kronenwett, et al.. (2012). RNA-based determination of ESR1 and HER2 expression and response to neoadjuvant chemotherapy. Annals of Oncology. 24(3). 632–639. 19 indexed citations
12.
Müller, Berit, Jan C. Brase, Karsten E. Weber, et al.. (2012). Comparison of the RNA-based EndoPredict multigene test between core biopsies and corresponding surgical breast cancer sections. Journal of Clinical Pathology. 65(7). 660–662. 25 indexed citations
13.
Sinn, Bruno V., Gϋnter von Minckwitz, Carsten Denkert, et al.. (2012). 8O_PR MUCIN-1 Protein and Mrna Expression in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy. Annals of Oncology. 23. ii17–ii17.
14.
Kotoula, Vassiliki, Konstantine T. Kalogeras, George Kouvatseas, et al.. (2012). Sample parameters affecting the clinical relevance of RNA biomarkers in translational breast cancer research. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 462(2). 141–154. 13 indexed citations
15.
Dubsky, Peter, Martin Filipits, R. Jakesz, et al.. (2012). EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Annals of Oncology. 24(3). 640–647. 129 indexed citations
16.
Müller, Berit, Ralf Kronenwett, Guido Hennig, et al.. (2011). Quantitative Determination of Estrogen Receptor, Progesterone Receptor, and HER2 mRNA in Formalin-fixed Paraffin-embedded Tissue—A New Option for Predictive Biomarker Assessment in Breast Cancer. Diagnostic Molecular Pathology. 20(1). 1–10. 68 indexed citations
17.
Klimke, A., et al.. (2010). Clozapine Mobilizes CD34+ Hematopoietic Stem and Progenitor Cells and Increases Plasma Concentration of Interleukin 6 in Patients With Schizophrenia. Journal of Clinical Psychopharmacology. 30(5). 591–595. 28 indexed citations
18.
Noske, Aurelia, Sibylle Loibl, Silvia Darb‐Esfahani, et al.. (2010). Comparison of different approaches for assessment of HER2 expression on protein and mRNA level: prediction of chemotherapy response in the neoadjuvant GeparTrio trial (NCT00544765). Breast Cancer Research and Treatment. 126(1). 109–117. 47 indexed citations
19.
Steidl, Ulrich, Roland Fenk, Ingmar Bruns, et al.. (2004). Successful transplantation of peripheral blood stem cells mobilized by chemotherapy and a single dose of pegylated G-CSF in patients with multiple myeloma. Bone Marrow Transplantation. 35(1). 33–36. 68 indexed citations
20.
Heyd, Florian, Ulrich Steidl, Roland Fenk, et al.. (2003). Non-small lung cancer cells are prime targets for p53 gene transfer mediated by a recombinant adeno-associated virus type-2 vector. Cancer Gene Therapy. 10(12). 898–906. 10 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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