Penelope A. Bradbury

4.3k total citations
112 papers, 2.0k citations indexed

About

Penelope A. Bradbury is a scholar working on Pulmonary and Respiratory Medicine, Oncology and Cancer Research. According to data from OpenAlex, Penelope A. Bradbury has authored 112 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Pulmonary and Respiratory Medicine, 68 papers in Oncology and 25 papers in Cancer Research. Recurrent topics in Penelope A. Bradbury's work include Lung Cancer Treatments and Mutations (64 papers), Lung Cancer Research Studies (28 papers) and Cancer Genomics and Diagnostics (22 papers). Penelope A. Bradbury is often cited by papers focused on Lung Cancer Treatments and Mutations (64 papers), Lung Cancer Research Studies (28 papers) and Cancer Genomics and Diagnostics (22 papers). Penelope A. Bradbury collaborates with scholars based in Canada, United States and Switzerland. Penelope A. Bradbury's co-authors include Frances A. Shepherd, Natasha B. Leighl, Geoffrey Liu, Ming‐Sound Tsao, Lesley Seymour, Ronald Feld, Keyue Ding, Dongsheng Tu, Peter Ellis and Adrian G. Sacher and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and JNCI Journal of the National Cancer Institute.

In The Last Decade

Penelope A. Bradbury

100 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Penelope A. Bradbury Canada 29 1.1k 1.1k 604 419 170 112 2.0k
Delphine Borchiellini France 22 1.2k 1.0× 986 0.9× 639 1.1× 458 1.1× 300 1.8× 89 1.8k
Harpreet Singh United States 24 647 0.6× 1.0k 1.0× 495 0.8× 427 1.0× 97 0.6× 101 1.8k
Sibyl Anderson United States 19 1.2k 1.1× 695 0.6× 944 1.6× 524 1.3× 218 1.3× 40 2.1k
Christian Rothermundt Switzerland 25 971 0.9× 1.0k 0.9× 755 1.3× 371 0.9× 265 1.6× 79 2.3k
Xun Lin United States 23 1.4k 1.3× 943 0.9× 823 1.4× 617 1.5× 206 1.2× 94 2.2k
Katherine Liau United States 19 843 0.8× 1.1k 1.0× 861 1.4× 441 1.1× 104 0.6× 36 2.0k
Christine Lydon United States 18 1.2k 1.1× 1.0k 1.0× 658 1.1× 435 1.0× 75 0.4× 30 1.8k
Alex Friedlaender Switzerland 28 1.1k 1.0× 1.5k 1.3× 711 1.2× 437 1.0× 131 0.8× 86 2.3k
Jessica Menis Italy 21 1.1k 1.0× 1.2k 1.1× 381 0.6× 363 0.9× 145 0.9× 76 1.9k
Ulrika Harmenberg Sweden 23 1.0k 0.9× 736 0.7× 693 1.1× 375 0.9× 208 1.2× 57 1.7k

Countries citing papers authored by Penelope A. Bradbury

Since Specialization
Citations

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

Fields of papers citing papers by Penelope A. Bradbury

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Penelope A. Bradbury. 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 Penelope A. Bradbury. The network helps show where Penelope A. Bradbury may publish in the future.

Co-authorship network of co-authors of Penelope A. Bradbury

This figure shows the co-authorship network connecting the top 25 collaborators of Penelope A. Bradbury. A scholar is included among the top collaborators of Penelope A. Bradbury 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 Penelope A. Bradbury. Penelope A. Bradbury 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.
Katzberg, Hans, Vera Bril, Warren Mason, et al.. (2024). Myasthenia Gravis in Patients Treated With Immune Checkpoint Inhibitors. JTO Clinical and Research Reports. 6(5). 100772–100772.
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Feng, Jiao, Katrina Hueniken, Zhenzhen Fan, et al.. (2023). 1353P Effect of TP53 co-mutation in non-small cell lung cancer (NSCLC) with driver mutations. Annals of Oncology. 34. S778–S778. 1 indexed citations
4.
Anagnostou, Valsamo, Cheryl Ho, Garth Nicholas, et al.. (2023). ctDNA response after pembrolizumab in non-small cell lung cancer: phase 2 adaptive trial results. Nature Medicine. 29(10). 2559–2569. 66 indexed citations
5.
Fung, Andrea S., Jessica Weiss, Geoffrey Liu, et al.. (2023). Programmed Cell Death Protein 1 Inhibitors and MET Targeted Therapies in NSCLC With MET Exon 14 Skipping Mutations: Efficacy and Toxicity as Sequential Therapies. JTO Clinical and Research Reports. 4(10). 100562–100562. 4 indexed citations
6.
Mansfield, Aaron S., Penelope A. Bradbury, David J. Kwiatkowski, et al.. (2023). OA02.04 Phase 1/2 Randomized Trial of Anetumab Ravtansine and Pembrolizumab Compared to Pembrolizumab for Pleural Mesothelioma - NCT03126630. Journal of Thoracic Oncology. 18(11). S47–S47. 2 indexed citations
7.
Schmid, Sabine, Miguel García-Pardo, Sierra Cheng, et al.. (2022). Treatment patterns and outcomes in early-stage ALK-rearranged non-small cell lung cancer. Lung Cancer. 166. 58–62. 7 indexed citations
8.
Cheng, Michael L., Christie J. Lau, Marina S.D. Milan, et al.. (2021). Plasma ctDNA Response Is an Early Marker of Treatment Effect in Advanced NSCLC. JCO Precision Oncology. 5(5). 393–402. 20 indexed citations
9.
Lau, Sally C. M., Aline Fusco Fares, Lisa W. Le, et al.. (2021). Subtypes of EGFR- and HER2-Mutant Metastatic NSCLC Influence Response to Immune Checkpoint Inhibitors. Clinical Lung Cancer. 22(4). 253–259. 54 indexed citations
10.
O’Kane, Grainne M., Geoffrey Liu, Tracy Stockley, et al.. (2019). The presence and variant allele fraction of EGFR mutations in ctDNA and development of resistance. Lung Cancer. 131. 86–89. 14 indexed citations
11.
Pal, Prodipto, Aline Fusco Fares, Erin Stewart, et al.. (2019). MA18.07 Identification of Neuroendocrine Transformation in Anaplastic Lymphoma Kinase Rearranged (ALK+) Tumors After Tyrosine Kinase Inhibitors. Journal of Thoracic Oncology. 14(10). S323–S324.
12.
Stockley, Tracy, Ming‐Sound Tsao, Joshua C. Morganstein, et al.. (2018). P2.03-03 Upfront Next Generation Sequencing in NSCLC: A Publicly Funded Perspective. Journal of Thoracic Oncology. 13(10). S717–S717.
13.
Cabanero, Michael, Mor Moskovitz, Jonathan M. Weiss, et al.. (2018). P3.03-29 The Prognostic Effect of Tumor Mutation Burden and Smoking History in Resected EGFR Mutant Non-Small Cell Lung Cancer. Journal of Thoracic Oncology. 13(10). S921–S921. 1 indexed citations
14.
Hueniken, Katrina, M. Catherine Brown, Liang Mao, et al.. (2018). MA18.09 Predictors of Health Utility Scores (HUS) in Advanced EGFR-Mutated NSCLC. Journal of Thoracic Oncology. 13(10). S420–S421. 1 indexed citations
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Juergens, Rosalyn A., Desirée Hao, Scott A. Laurie, et al.. (2017). MA09.03 Cisplatin/Pemetrexed + Durvalumab +/- Tremelimumab in Pts with Advanced Non-Squamous NSCLC: A CCTG Phase IB Study - IND.226. Journal of Thoracic Oncology. 12(1). S392–S393. 11 indexed citations
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Wong, Kit Man, Keyue Ding, Suzanne C. Li, et al.. (2016). A Cost-Effectiveness Analysis of Using the JBR.10-Based 15-Gene Expression Signature to Guide Adjuvant Chemotherapy in Early Stage Non–Small-Cell Lung Cancer. Clinical Lung Cancer. 18(1). e41–e47. 3 indexed citations
20.
Foster, Nathan R., Lindsay A. Renfro, Steven E. Schild, et al.. (2015). Multitrial Evaluation of Progression-Free Survival as a Surrogate End Point for Overall Survival in First-Line Extensive-Stage Small-Cell Lung Cancer. Journal of Thoracic Oncology. 10(7). 1099–1106. 33 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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