Catherine Dubos Arvis

8.3k total citations · 1 hit paper
16 papers, 5.3k citations indexed

About

Catherine Dubos Arvis is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Catherine Dubos Arvis has authored 16 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Oncology, 8 papers in Pulmonary and Respiratory Medicine and 4 papers in Cancer Research. Recurrent topics in Catherine Dubos Arvis's work include Cancer Immunotherapy and Biomarkers (11 papers), Lung Cancer Treatments and Mutations (7 papers) and Colorectal Cancer Treatments and Studies (6 papers). Catherine Dubos Arvis is often cited by papers focused on Cancer Immunotherapy and Biomarkers (11 papers), Lung Cancer Treatments and Mutations (7 papers) and Colorectal Cancer Treatments and Studies (6 papers). Catherine Dubos Arvis collaborates with scholars based in France, Spain and South Korea. Catherine Dubos Arvis's co-authors include Paul Baas, Margarita Majem, Edward B. Garon, Gregory M. Lubiniecki, Roy S. Herbst, Ji‐Youn Han, José Luis Perez‐Gracia, Dong‐Wan Kim, Marcelo Garrido and Gilberto de Castro and has published in prestigious journals such as The Lancet, Journal of Clinical Oncology and Annals of Oncology.

In The Last Decade

Catherine Dubos Arvis

16 papers receiving 5.2k 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.

Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial

2015 4.9k citations Roy S. Herbst, Paul Baas et al. The Lancet profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Catherine Dubos Arvis France	11	4.8k	3.2k	1.0k	585	444	16	5.3k
Achim Rittmeyer United States	26	4.9k 1.0×	3.5k 1.1×	1.1k 1.0×	687 1.2×	593 1.3×	91	5.7k
Ellie Im United States	13	4.9k 1.0×	3.1k 1.0×	1.1k 1.1×	650 1.1×	510 1.1×	31	5.5k
Mary J. Fidler United States	22	5.2k 1.1×	3.7k 1.2×	1.1k 1.1×	748 1.3×	803 1.8×	115	6.4k
Gene Grant Finley United States	19	3.3k 0.7×	2.2k 0.7×	690 0.7×	466 0.8×	961 2.2×	70	4.4k
Daniel Waterkamp United States	21	3.5k 0.7×	1.9k 0.6×	813 0.8×	530 0.9×	311 0.7×	39	3.9k
Melanie A. Leiby United States	6	6.1k 1.3×	3.9k 1.2×	1.5k 1.5×	930 1.6×	795 1.8×	7	7.2k
Laura Mezquita France	28	2.5k 0.5×	1.6k 0.5×	573 0.6×	419 0.7×	460 1.0×	174	3.3k
Manuel Cobo Spain	30	3.6k 0.8×	3.4k 1.1×	565 0.5×	1.1k 1.9×	1.3k 3.0×	219	5.3k
Willemijn S.M.E. Theelen Netherlands	16	3.0k 0.6×	3.3k 1.0×	385 0.4×	873 1.5×	998 2.2×	33	4.4k
Francesco Orlandi United States	17	4.9k 1.0×	3.2k 1.0×	853 0.8×	565 1.0×	806 1.8×	39	5.6k

Countries citing papers authored by Catherine Dubos Arvis

Since Specialization

Citations

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

Fields of papers citing papers by Catherine Dubos Arvis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Catherine Dubos Arvis. 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 Catherine Dubos Arvis. The network helps show where Catherine Dubos Arvis may publish in the future.

Co-authorship network of co-authors of Catherine Dubos Arvis

This figure shows the co-authorship network connecting the top 25 collaborators of Catherine Dubos Arvis. A scholar is included among the top collaborators of Catherine Dubos Arvis 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 Catherine Dubos Arvis. Catherine Dubos Arvis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Herbst, Roy S., Edward B. Garon, Dong‐Wan Kim, et al.. (2020). Long-Term Outcomes and Retreatment Among Patients With Previously Treated, Programmed Death-Ligand 1‒Positive, Advanced Non‒Small-Cell Lung Cancer in the KEYNOTE-010 Study. Journal of Clinical Oncology. 38(14). 1580–1590. 201 indexed citations

Schüler, Martin, Eng-Huat Tan, Kenneth J. O’Byrne, et al.. (2019). First-line afatinib vs gefitinib for patients with EGFR mutation-positive NSCLC (LUX-Lung 7): impact of afatinib dose adjustment and analysis of mode of initial progression for patients who continued treatment beyond progression. Journal of Cancer Research and Clinical Oncology. 145(6). 1569–1579. 31 indexed citations

Guigay, J., Jérôme Fayette, Ricard Mesı́a, et al.. (2019). TPExtreme randomized trial: TPEx versus Extreme regimen in 1st line recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC).. Journal of Clinical Oncology. 37(15_suppl). 6002–6002. 46 indexed citations

Herbst, Roy S., Edward B. Garon, D.-W. Kim, et al.. (2018). Long-term survival in patients (pts) with advanced NSCLC in the KEYNOTE-010 study overall and in pts who completed two years of pembrolizumab (pembro). Annals of Oncology. 29. viii749–viii749. 17 indexed citations

Herbst, Roy S., Edward B. Garon, D.-W. Kim, et al.. (2018). Long-term follow-up in the KEYNOTE-010 study of pembrolizumab (pembro) for advanced NSCLC, including in patients (pts) who completed 2 years of pembro and pts who received a second course of pembro. Annals of Oncology. 29. x42–x43. 22 indexed citations

Herbst, Roy S., Paul Baas, Dong‐Wan Kim, et al.. (2017). Factors associated with better overall survival (OS) in patients with previously treated, PD-L1–expressing, advanced NSCLC: Multivariate analysis of KEYNOTE-010.. Journal of Clinical Oncology. 35(15_suppl). 9090–9090. 16 indexed citations

Heutte, Natacha, Véronique André, Catherine Dubos Arvis, et al.. (2016). Assessment of multi-contaminant exposure in a cancer treatment center: a 2-year monitoring of molds, mycotoxins, endotoxins, and glucans in bioaerosols. Environmental Monitoring and Assessment. 189(1). 31–31. 23 indexed citations

Herbst, Roy S., Paul Baas, José Luis Perez‐Gracia, et al.. (2016). P2.41 (also presented as PD1.06): Pembrolizumab vs Docetaxel for Previously Treated NSCLC (KEYNOTE-010): Archival vs New Tumor Samples for PD-L1 Assessment. Journal of Thoracic Oncology. 11(10). S242–S243. 7 indexed citations

Herbst, Roy S., Paul Baas, José Luis Perez‐Gracia, et al.. (2016). PD1.06 (also presented as P2.41): Pembrolizumab vs Docetaxel for Previously Treated NSCLC (KEYNOTE-010): Archival vs New Tumor Samples for PD-L1 Assessment. Journal of Thoracic Oncology. 11(10). S174–S175. 2 indexed citations

10.

Schüler, Martin, Kenneth J. O’Byrne, L. Zhang, et al.. (2016). Time-to-treatment failure (TTF) with first-line afatinib (A) vs gefitinib (G) in patients (pts) with EGFR mutation-positive (EGFRm+) advanced non-small-cell lung cancer (NSCLC): Randomized phase IIb LUX-lung 7 (LL7) trial. Annals of Oncology. 27. vi425–vi425. 1 indexed citations

11.

Herbst, Roy S., Paul Baas, Dongwook Kim, et al.. (2016). Pembrolizumab (pembro) vs docetaxel (doce) for previously treated, PD-L1–expressing NSCLC: Updated outcomes of KEYNOTE-010. Annals of Oncology. 27. vi583–vi583. 6 indexed citations

12.

Baas, Paul, Edward B. Garon, Roy S. Herbst, et al.. (2016). Relationship between level of PD-L1 expression and outcomes in the KEYNOTE-010 study of pembrolizumab vs docetaxel for previously treated, PD-L1–Positive NSCLC.. Journal of Clinical Oncology. 34(15_suppl). 9015–9015. 11 indexed citations

13.

Hirsh, Vera, James Chih‐Hsin Yang, Kenneth J. O’Byrne, et al.. (2016). First-line afatinib (A) vs gefitinib (G) for patients (pts) with EGFR mutation positive (EGFRm+) NSCLC (LUX-Lung 7): Patient-reported outcomes (PROs) and impact of dose modifications on efficacy and adverse events (AEs).. Journal of Clinical Oncology. 34(15_suppl). 9046–9046. 11 indexed citations

14.

Garon, Edward B., Roy S. Herbst, Dong‐Wan Kim, et al.. (2016). Pembrolizumab vs docetaxel for previously treated advanced NSCLC with a PD-L1 tumor proportion score (TPS) 1%-49%: Results from KEYNOTE-010.. Journal of Clinical Oncology. 34(15_suppl). 9024–9024. 12 indexed citations

15.

Herbst, Roy S., Paul Baas, José Luis Perez‐Gracia, et al.. (2016). Archival vs new tumor samples for assessing PD-L1 expression in the KEYNOTE-010 study of pembrolizumab (pembro) vs docetaxel (doce) for previously treated advanced NSCLC.. Journal of Clinical Oncology. 34(15_suppl). 3030–3030. 4 indexed citations

16.

Herbst, Roy S., Paul Baas, Dong‐Wan Kim, et al.. (2015). Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. The Lancet. 387(10027). 1540–1550. 4873 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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