Lida Pacaud
Impact in
- Oncology top 5%
- CAR-T cell therapy research
- HER2/EGFR in Cancer Research
- Hematology top 10%
- Multiple Myeloma Research and Treatments
Papers in
- Oncology 43
- CAR-T cell therapy research 35
- Lung Cancer Research Studies 4
- Hematology 29
- Multiple Myeloma Research and Treatments 28
- Co-authors
- Tetiana Taran (1 shared paper)Fabrice André (2 shared papers)Zhimin Shao (1 shared paper)Qingyuan Zhang (1 shared paper)Silvia P. Neciosup (1 shared paper)Masakazu Toi (2 shared papers)Howard A. Burris (2 shared papers)Marc Buyse (2 shared papers)
- Journals
- Blood (11 papers)Cancer Research (5 papers)HemaSphere (4 papers)Hematological Oncology (3 papers)Future Oncology (2 papers)
- Partner nations
- United StatesBelgiumSwitzerland
In The Last Decade
Lida Pacaud
53 papers receiving 649 citations
Peers
Comparison fields: 5 of 42
- Oncology 487
- Hematology 105
- Radiology, Nuclear Medicine and Imaging 74
- Immunology 67
- Genetics 32
Countries citing papers authored by Lida Pacaud
This map shows the geographic impact of Lida Pacaud'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 Lida Pacaud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lida Pacaud more than expected).
Fields of papers citing papers by Lida Pacaud
This network shows the impact of papers produced by Lida Pacaud. 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 Lida Pacaud. The network helps show where Lida Pacaud may publish in the future.
Co-authors
The 25 scholars most cited alongside Lida Pacaud, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 244 | |
| 2 | 2019 | 62 | |
| 3 | 2019 | 58 | |
| 4 | 2021 | 36 | |
| 5 | 2021 | 30 | |
| 6 | 2021 | 20 | |
| 7 | 2020 | 18 | |
| 8 | 2019 | 17 | |
| 9 | 2022 | 15 | |
| 10 | 2022 | 13 | |
| 11 | 2023 | 12 | |
| 12 | 2021 | 12 | |
| 13 | 2023 | 9 | |
| 14 | 2021 | 9 | |
| 15 | 2022 | 8 | |
| 16 | 2023 | 7 | |
| 17 | 2022 | 6 | |
| 18 | 2023 | 5 | |
| 19 | 2022 | 5 | |
| 20 | 2023 | 4 |
About Lida Pacaud
Lida Pacaud is a scholar working on Oncology, Hematology, Immunology, Molecular Biology and Radiology, Nuclear Medicine and Imaging, having authored 57 papers that have together received 660 indexed citations. Recurring topics across this work include CAR-T cell therapy research (35 papers), Multiple Myeloma Research and Treatments (28 papers), Biosimilars and Bioanalytical Methods (13 papers), Protein Degradation and Inhibitors (8 papers), Monoclonal and Polyclonal Antibodies Research (5 papers), Lung Cancer Research Studies (4 papers), Immunotherapy and Immune Responses (4 papers) and Lymphoma Diagnosis and Treatment (4 papers). The work is most often cited by research in Oncology (487 citations), Hematology (105 citations), Radiology, Nuclear Medicine and Imaging (74 citations), Immunology (67 citations) and Genetics (32 citations). Lida Pacaud has collaborated with scholars based in United States, Belgium and Switzerland. Frequent co-authors include Tetiana Taran, Fabrice André, Zhimin Shao, Qingyuan Zhang, Silvia P. Neciosup, Masakazu Toi, Howard A. Burris, Marc Buyse, Donggeng Liu and Max S. Mano. Their work appears in journals such as Blood, Cancer Research, HemaSphere, Hematological Oncology and Future Oncology.
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.