Jorge J. Nieva

8.7k total citations
163 papers, 4.3k citations indexed

About

Jorge J. Nieva is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Jorge J. Nieva has authored 163 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Oncology, 76 papers in Pulmonary and Respiratory Medicine and 55 papers in Cancer Research. Recurrent topics in Jorge J. Nieva's work include Lung Cancer Treatments and Mutations (59 papers), Cancer Genomics and Diagnostics (48 papers) and Cancer Cells and Metastasis (24 papers). Jorge J. Nieva is often cited by papers focused on Lung Cancer Treatments and Mutations (59 papers), Cancer Genomics and Diagnostics (48 papers) and Cancer Cells and Metastasis (24 papers). Jorge J. Nieva collaborates with scholars based in United States, United Kingdom and Canada. Jorge J. Nieva's co-authors include Kelly Bethel, Peter Kühn, Paul Wentworth, Richard A. Lerner, Lyudmila Bazhenova, Dena Marrinucci, Anita D. Wentworth, Alan Saven, Albert Eschenmoser and Qinghai Zhang and has published in prestigious journals such as Science, New England Journal of Medicine and Proceedings of the National Academy of Sciences.

In The Last Decade

Jorge J. Nieva

148 papers receiving 4.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jorge J. Nieva United States 35 1.6k 1.2k 931 850 506 163 4.3k
Mourad Tighiouart United States 38 1.7k 1.1× 1.6k 1.4× 700 0.8× 930 1.1× 209 0.4× 152 5.0k
David G. Menter United States 43 2.6k 1.6× 2.1k 1.8× 1.4k 1.5× 984 1.2× 359 0.7× 136 5.9k
Jacintha O’Sullivan Ireland 41 1.7k 1.0× 2.2k 1.9× 1.2k 1.3× 697 0.8× 226 0.4× 171 5.3k
Guido Bocci Italy 42 2.6k 1.6× 2.4k 2.1× 1.2k 1.3× 1.0k 1.2× 247 0.5× 206 5.8k
Feng Ye China 32 740 0.5× 2.3k 1.9× 766 0.8× 451 0.5× 412 0.8× 148 4.7k
Wolfgang Hilbe Austria 33 1.3k 0.8× 1.2k 1.0× 448 0.5× 794 0.9× 200 0.4× 129 3.9k
Trond Stokke Norway 37 1.2k 0.8× 3.0k 2.6× 1.0k 1.1× 680 0.8× 204 0.4× 164 5.3k
Balázs Döme Hungary 44 2.1k 1.3× 2.6k 2.3× 1.1k 1.2× 1.4k 1.6× 125 0.2× 199 5.7k
Joseph Ciccolini France 36 2.4k 1.5× 1.6k 1.4× 677 0.7× 683 0.8× 164 0.3× 196 4.5k
Ying Yang China 41 1.7k 1.0× 2.2k 1.9× 1.3k 1.3× 868 1.0× 226 0.4× 136 5.1k

Countries citing papers authored by Jorge J. Nieva

Since Specialization
Citations

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

Fields of papers citing papers by Jorge J. Nieva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jorge J. Nieva. 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 Jorge J. Nieva. The network helps show where Jorge J. Nieva may publish in the future.

Co-authorship network of co-authors of Jorge J. Nieva

This figure shows the co-authorship network connecting the top 25 collaborators of Jorge J. Nieva. A scholar is included among the top collaborators of Jorge J. Nieva 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 Jorge J. Nieva. Jorge J. Nieva 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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Gitlitz, Barbara J., Vinay Duddalwar, Kevin G. King, et al.. (2024). Improved efficacy of pembrolizumab combined with soluble EphB4-albumin in HPV-negative EphrinB2 positive head neck squamous cell carcinoma. Oncotarget. 15(1). 444–458.
4.
Zhao, Ziwei, et al.. (2024). Single breath‐hold volumetric lung imaging at 0.55T using stack‐of‐spiral (SoS) out‐in balanced SSFP. Magnetic Resonance in Medicine. 93(5). 1999–2007.
5.
Elliott, Andrew, Wendy Cozen, Evanthia T. Roussos Torres, et al.. (2024). Survival and mutational differences based on ESR1 and ESR2 expression in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology. 42(16_suppl). 8526–8526. 1 indexed citations
6.
Goldsack, Jennifer C., Cindy Geoghegan, Jorge J. Nieva, et al.. (2023). Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care. Journal of Medical Internet Research. 25. e43404–e43404. 21 indexed citations
7.
Reuss, Joshua E., Nishant Gandhi, Phillip Walker, et al.. (2023). Spectrum of acquired KRAS mutations in driver mutation-positive non-small cell lung cancer.. Journal of Clinical Oncology. 41(16_suppl). 9069–9069.
8.
Marmarelis, Melina E., Caroline E. McCoach, Geoffrey Liu, et al.. (2023). Characteristics of long-term survivors with EGFR mutant (EGFRm) metastatic non-small cell lung cancer (mNSCLC).. Journal of Clinical Oncology. 41(16_suppl). 9116–9116.
9.
Pérol, M., Nong Yang, Yuichiro Ohe, et al.. (2023). 1373P Efficacy and safety of taletrectinib in patients (Pts) with ROS1+ non-small cell lung cancer (NSCLC): Interim analysis of global TRUST-II study. Annals of Oncology. 34. S788–S789. 9 indexed citations
10.
Wojcik, Katherine Y., et al.. (2023). Characterization of mortality and high-risk characteristics of thyroid cancer in Filipinos using the California Cancer Registry. Frontiers in Public Health. 10. 1104607–1104607.
11.
Iams, Wade T., Sharon Phillips, Adrian G. Sacher, et al.. (2023). A Multicenter Retrospective Chart Review of Clinical Outcomes Among Patients With KRAS G12C Mutant Non–Small Cell Lung Cancer. Clinical Lung Cancer. 24(3). 228–234. 2 indexed citations
12.
Shishido, Stephanie N., Lisa Welter, Mariam Rodrı́guez-Lee, et al.. (2020). Preanalytical Variables for the Genomic Assessment of the Cellular and Acellular Fractions of the Liquid Biopsy in a Cohort of Breast Cancer Patients. Journal of Molecular Diagnostics. 22(3). 319–337. 25 indexed citations
13.
Jonna, Sushma, Rebecca Feldman, Jeffrey Swensen, et al.. (2019). Detection of NRG1 Gene Fusions in Solid Tumors. Clinical Cancer Research. 25(16). 4966–4972. 155 indexed citations
14.
Barber, Daniel L., Shunsuke Sakai, Ragini R. Kudchadkar, et al.. (2019). Tuberculosis following PD-1 blockade for cancer immunotherapy. Science Translational Medicine. 11(475). 132 indexed citations
15.
Boffa, Daniel J., Ryon P. Graf, Michelle C. Salazar, et al.. (2017). Cellular Expression of PD-L1 in the Peripheral Blood of Lung Cancer Patients is Associated with Worse Survival. Cancer Epidemiology Biomarkers & Prevention. 26(7). 1139–1145. 61 indexed citations
16.
Nieva, Jorge J., et al.. (2017). Biophysical technologies for understanding circulating tumor cell biology and metastasis. Translational Lung Cancer Research. 6(4). 473–485. 15 indexed citations
17.
In, Gino K. & Jorge J. Nieva. (2015). Emerging chemotherapy agents in lung cancer: nanoparticles therapeutics for non-small cell lung cancer. Translational Cancer Research. 4(4). 340–355. 13 indexed citations
18.
Carlsson, Anders, Viswam S. Nair, Madelyn Luttgen, et al.. (2014). Circulating Tumor Microemboli Diagnostics for Patients with Non–Small-Cell Lung Cancer. Journal of Thoracic Oncology. 9(8). 1111–1119. 59 indexed citations
19.
Newton, Paul K., Jeremy Mason, Kelly Bethel, et al.. (2013). Spreaders and Sponges Define Metastasis in Lung Cancer: A Markov Chain Monte Carlo Mathematical Model. Cancer Research. 73(9). 2760–2769. 66 indexed citations
20.
Wentworth, Paul, Jonathan E. McDunn, Anita D. Wentworth, et al.. (2002). Evidence for Antibody-Catalyzed Ozone Formation in Bacterial Killing and Inflammation. Science. 298(5601). 2195–2199. 285 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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