Jens Huober

33.1k total citations · 4 hit papers
321 papers, 12.1k citations indexed

About

Jens Huober is a scholar working on Oncology, Cancer Research and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Jens Huober has authored 321 papers receiving a total of 12.1k indexed citations (citations by other indexed papers that have themselves been cited), including 250 papers in Oncology, 175 papers in Cancer Research and 114 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Jens Huober's work include Breast Cancer Treatment Studies (149 papers), HER2/EGFR in Cancer Research (109 papers) and Cancer Treatment and Pharmacology (98 papers). Jens Huober is often cited by papers focused on Breast Cancer Treatment Studies (149 papers), HER2/EGFR in Cancer Research (109 papers) and Cancer Treatment and Pharmacology (98 papers). Jens Huober collaborates with scholars based in Germany, Switzerland and United States. Jens Huober's co-authors include Sibylle Loibl, Gϋnter von Minckwitz, Michael Untch, Christian Jackisch, Holger Eidtmann, J. Hilfrich, Jens‐Uwe Blohmer, Carsten Denkert, Peter A. Fasching and Bernd Gerber and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and JNCI Journal of the National Cancer Institute.

In The Last Decade

Jens Huober

311 papers receiving 11.9k citations

Hit Papers

Definition and Impact of ... 2012 2026 2016 2021 2012 2017 2018 2022 500 1000 1.5k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jens Huober 8.6k 6.0k 3.3k 1.9k 1.7k 321 12.1k
Christian Jackisch 8.5k 1.0× 6.2k 1.0× 1.8k 0.6× 2.2k 1.2× 1.8k 1.0× 356 12.6k
P. Fumoleau 9.6k 1.1× 4.4k 0.7× 2.7k 0.8× 1.1k 0.6× 3.3k 1.9× 326 14.3k
Elżbieta Senkus 6.2k 0.7× 4.0k 0.7× 2.5k 0.8× 1.2k 0.6× 2.3k 1.4× 113 10.1k
Angelo Di Leo 9.9k 1.2× 5.2k 0.9× 4.5k 1.4× 702 0.4× 4.1k 2.4× 313 14.4k
Charles E. Geyer 11.3k 1.3× 8.6k 1.4× 3.2k 1.0× 2.9k 1.6× 2.5k 1.5× 183 16.3k
Semiglazov Vf 8.5k 1.0× 5.6k 0.9× 2.1k 0.6× 1.5k 0.8× 2.1k 1.2× 239 11.9k
Young‐Hyuck Im 8.7k 1.0× 3.8k 0.6× 3.7k 1.1× 1.2k 0.7× 2.9k 1.7× 270 12.4k
Jennifer K. Litton 7.7k 0.9× 3.8k 0.6× 2.4k 0.7× 1.1k 0.6× 3.2k 1.8× 238 11.1k
Aňa Lluch 10.3k 1.2× 6.1k 1.0× 2.5k 0.7× 1.2k 0.6× 4.1k 2.4× 306 14.7k
Constance Cirrincione 6.8k 0.8× 4.7k 0.8× 1.6k 0.5× 1.6k 0.8× 1.3k 0.8× 81 9.8k

Countries citing papers authored by Jens Huober

Since Specialization
Citations

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

Fields of papers citing papers by Jens Huober

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jens Huober

This figure shows the co-authorship network connecting the top 25 collaborators of Jens Huober. A scholar is included among the top collaborators of Jens Huober 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 Jens Huober. Jens Huober 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.
Goetz, Matthew P., İrfan Çiçin, Laura Testa, et al.. (2024). Impact of dose reductions on adjuvant abemaciclib efficacy for patients with high-risk early breast cancer: analyses from the monarchE study. npj Breast Cancer. 10(1). 34–34. 12 indexed citations
2.
O’Shaughnessy, Joyce, Laura Testa, Sara M. Tolaney, et al.. (2023). 274P Impact of dose reductions on efficacy of adjuvant abemaciclib for patients with high-risk early breast cancer (EBC): Analyses from the monarchE study. Annals of Oncology. 34. S293–S293. 1 indexed citations
4.
Jarczok, Marc N., et al.. (2023). Enhancing coping skills through brief interventions during cancer therapy – a quasi-experimental clinical pilot study. Frontiers in Psychology. 14. 1253423–1253423. 1 indexed citations
5.
Schmidt, Marcus, Ulrike Nitz, Toralf Reimer, et al.. (2023). Adjuvant capecitabine versus nihil in older patients with node-positive/high-risk node-negative early breast cancer receiving ibandronate – The ICE randomized clinical trial. European Journal of Cancer. 194. 113324–113324. 1 indexed citations
6.
Bekes, Inga & Jens Huober. (2023). Extended Adjuvant Endocrine Therapy in Early Breast Cancer Patients—Review and Perspectives. Cancers. 15(16). 4190–4190. 11 indexed citations
8.
Wildiers, Hans, Anne Armstrong, Eveline Cuypere, et al.. (2023). Paclitaxel plus Eftilagimod Alpha, a Soluble LAG-3 Protein, in Metastatic, HR+ Breast Cancer: Results from AIPAC, a Randomized, Placebo Controlled Phase IIb Trial. Clinical Cancer Research. 30(3). 532–541. 12 indexed citations
10.
Johnston, Stephen, Joyce O’Shaughnessy, Miguel Martín, et al.. (2021). Abemaciclib as initial therapy for advanced breast cancer: MONARCH 3 updated results in prognostic subgroups. npj Breast Cancer. 7(1). 80–80. 36 indexed citations
11.
Cosimo, Serena Di, Luca Porcu, Dominique Agbor‐Tarh, et al.. (2020). Effect of body mass index on response to neo-adjuvant therapy in HER2-positive breast cancer: an exploratory analysis of the NeoALTTO trial. Breast Cancer Research. 22(1). 115–115. 22 indexed citations
12.
Gregorio, Nikolaus de, Amelie de Gregorio, Florian Ebner, et al.. (2019). Pelvic exenteration as ultimate ratio for gynecologic cancers: single-center analyses of 37 cases. Archives of Gynecology and Obstetrics. 300(1). 161–168. 17 indexed citations
13.
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
14.
Riethdorf, Sabine, Volkmar Müller, Sibylle Loibl, et al.. (2017). Prognostic Impact of Circulating Tumor Cells for Breast Cancer Patients Treated in the Neoadjuvant "Geparquattro" Trial. Clinical Cancer Research. 23(18). 5384–5393. 82 indexed citations
16.
Baselga, José, Gail D. Lewis Phillips, Sunil Verma, et al.. (2016). Relationship between Tumor Biomarkers and Efficacy in EMILIA, a Phase III Study of Trastuzumab Emtansine in HER2-Positive Metastatic Breast Cancer. Clinical Cancer Research. 22(15). 3755–3763. 150 indexed citations
17.
Loibl, Sibylle, Silvia Darb‐Esfahani, Jens Huober, et al.. (2016). Integrated Analysis of PTEN and p4EBP1 Protein Expression as Predictors for pCR in HER2-Positive Breast Cancer. Clinical Cancer Research. 22(11). 2675–2683. 34 indexed citations
18.
Klauschen, Frederick, Stephan Wienert, Wolfgang Schmitt, et al.. (2014). Standardized Ki67 Diagnostics Using Automated Scoring—Clinical Validation in the GeparTrio Breast Cancer Study. Clinical Cancer Research. 21(16). 3651–3657. 78 indexed citations
19.
Minckwitz, Gϋnter von, Jens‐Uwe Blohmer, Serban Dan Costa, et al.. (2013). Response-Guided Neoadjuvant Chemotherapy for Breast Cancer. Journal of Clinical Oncology. 31(29). 3623–3630. 265 indexed citations
20.

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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