Lida Pacaud

9.0k total citations
56 papers, 621 citations indexed

About

Lida Pacaud is a scholar working on Oncology, Hematology and Immunology. According to data from OpenAlex, Lida Pacaud has authored 56 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Oncology, 31 papers in Hematology and 20 papers in Immunology. Recurrent topics in Lida Pacaud's work include CAR-T cell therapy research (36 papers), Multiple Myeloma Research and Treatments (30 papers) and Biosimilars and Bioanalytical Methods (14 papers). Lida Pacaud is often cited by papers focused on CAR-T cell therapy research (36 papers), Multiple Myeloma Research and Treatments (30 papers) and Biosimilars and Bioanalytical Methods (14 papers). Lida Pacaud collaborates with scholars based in United States, Belgium and Switzerland. Lida Pacaud's co-authors include Howard A. Burris, Masakazu Toi, Zefei Jiang, Qingyuan Zhang, Donggeng Liu, Dennis J. Slamon, Lydia Dreosti, Mary-Ann Lindsay, Silvia P. Neciosup and Zhimin Shao and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Lida Pacaud

51 papers receiving 614 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lida Pacaud United States 12 520 245 113 111 84 56 621
Melissa L. Comstock United States 6 254 0.5× 142 0.6× 137 1.2× 110 1.0× 106 1.3× 10 475
Naseem Kerr United Kingdom 6 606 1.2× 306 1.2× 131 1.2× 160 1.4× 59 0.7× 8 782
Cecilia Carpio Spain 15 388 0.7× 214 0.9× 110 1.0× 47 0.4× 62 0.7× 55 774
Michelle Blake United States 8 410 0.8× 442 1.8× 107 0.9× 52 0.5× 40 0.5× 11 670
Sreeni Yalamanchili United States 8 440 0.8× 266 1.1× 64 0.6× 57 0.5× 80 1.0× 17 634
Slava Stamova Germany 13 617 1.2× 163 0.7× 36 0.3× 43 0.4× 177 2.1× 22 752
Sabine Stienen Germany 13 491 0.9× 148 0.6× 34 0.3× 51 0.5× 276 3.3× 26 639
Linus Angenendt Germany 11 204 0.4× 173 0.7× 104 0.9× 25 0.2× 42 0.5× 29 404
Matthew Ku Australia 12 526 1.0× 97 0.4× 89 0.8× 42 0.4× 165 2.0× 47 821
Huimin Meng China 14 357 0.7× 209 0.9× 88 0.8× 14 0.1× 86 1.0× 34 582

Countries citing papers authored by Lida Pacaud

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Lida Pacaud

This figure shows the co-authorship network connecting the top 25 collaborators of Lida Pacaud. A scholar is included among the top collaborators of Lida Pacaud 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 Lida Pacaud. Lida Pacaud 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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Dhakal, Binod, Hang Quach, Andrew Spencer, et al.. (2024). Dose Escalation of ISB 1442, a Novel CD38 Biparatopic x CD47 Bispecific Antibody, in Patients with Relapsed / Refractory Multiple Myeloma. Blood. 144(Supplement 1). 3364–3364. 1 indexed citations
3.
Yong, Kwee, Hermann Einsele, Jordan M. Schecter, et al.. (2024). Characteristics and outcomes in patients with lenalidomide-refractory multiple myeloma treated with 1-3 prior lines of therapy: Analysis of individual patient-level data from daratumumab clinical trials. European Journal of Cancer. 215. 115157–115157. 2 indexed citations
4.
Dimopoulos, Meletios Α., Pieter Sonneveld, Salomon Manier, et al.. (2024). Progression-free survival as a surrogate endpoint for overall survival in patients with relapsed or refractory multiple myeloma. BMC Cancer. 24(1). 541–541. 1 indexed citations
5.
Lin, Yi, Thomas Martin, Saad Z. Usmani, et al.. (2023). POSTER: MM-309 CARTITUDE-1 Final Results: Phase 1b/2 Study of Ciltacabtagene Autoleucel in Heavily Pretreated Patients With Relapsed/Refractory Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 23. S226–S226. 1 indexed citations
6.
Jagannath, Sundar, Saad Z. Usmani, Jesús G. Berdeja, et al.. (2023). P-275 CARTITUDE-1 final results: phase 1b/2 study of ciltacabtagene autoleucel in heavily pretreated patients with relapsed/refractory multiple myeloma. Clinical Lymphoma Myeloma & Leukemia. 23. S187–S187. 2 indexed citations
7.
Mateos, María‐Victoria, Katja Weisel, Joris Diels, et al.. (2023). Characterization and Outcomes of Spanish Patients With Relapsed/Refractory Multiple Myeloma Included in the LocoMMotion Study. Clinical Lymphoma Myeloma & Leukemia. 24(4). 224–231.e2.
8.
Lin, Yi, Saad Z. Usmani, Jesús G. Berdeja, et al.. (2023). MM-309 CARTITUDE-1 Final Results: Phase 1b/2 Study of Ciltacabtagene Autoleucel in Heavily Pretreated Patients With Relapsed/Refractory Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 23. S488–S489.
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Ri, Masaki, Kenshi Suzuki, Tadao Ishida, et al.. (2022). Ciltacabtagene autoleucel in patients with relapsed/refractory multiple myeloma: CARTITUDE‐1 (phase 2) Japanese cohort. Cancer Science. 113(12). 4267–4276. 12 indexed citations
10.
Martin, Thomas, Carolyn C. Jackson, Lida Pacaud, Deepu Madduri, & Sundar Jagannath. (2022). Recent Advances in the Use of Chimeric Antigen Receptor–Expressing T-Cell Therapies for Treatment of Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 23(1). 22–27. 6 indexed citations
11.
Martin, Tom, Saad Z. Usmani, Jordan M. Schecter, et al.. (2021). Matching-adjusted indirect comparison of efficacy outcomes for ciltacabtagene autoleucel in CARTITUDE-1 versus idecabtagene vicleucel in KarMMa for the treatment of patients with relapsed or refractory multiple myeloma. Current Medical Research and Opinion. 37(10). 1779–1788. 34 indexed citations
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Jaeger, Ulrich, Nina Worel, Joseph P. McGuirk, et al.. (2019). Portia: A Phase 1b Study Evaluating Safety and Efficacy of Tisagenlecleucel and Pembrolizumab in Patients with Relapsed/Refractory Diffuse Large B-Cell Lymphoma. Blood. 134(Supplement_1). 5325–5325. 17 indexed citations
16.
Awasthi, Rakesh, Constantine S. Tam, Ulrich Jaeger, et al.. (2018). Clinical pharmacology of tisagenlecleucel (CTL019) in patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL). Clinical Trials. 78(13).
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Awasthi, Rakesh, Constantine S. Tam, Ulrich Jaeger, et al.. (2017). Clinical Pharmacology of CTL019 in Patients with Relapsed/Refractory (r/r) Diffuse Large B-Cell Lymphoma (DLBCL). Blood. 130. 5211–5211. 1 indexed citations
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Yao, James C., Nicola Fazio, Roberto Buzzoni, et al.. (2016). ORAL02.02: Efficacy and Safety of Everolimus in Advanced, Progressive, Nonfunctional Neuroendocrine Tumors (NET) of the Lung: RADIANT-4 Subgroup Analysis. Journal of Thoracic Oncology. 11(11). S253–S253. 2 indexed citations
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Yao, James C., Simron Singh, Edward M. Wolin, et al.. (2015). 134O RADIANT-4: Efficacy and safety of everolimus in advanced, nonfunctional neuroendocrine tumors (NET) of the lung or gastrointestinal (GI) tract. Annals of Oncology. 26. ix40–ix40. 2 indexed citations
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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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