Brian J. Sworder

2.1k total citations
34 papers, 719 citations indexed

About

Brian J. Sworder is a scholar working on Cancer Research, Oncology and Pathology and Forensic Medicine. According to data from OpenAlex, Brian J. Sworder has authored 34 papers receiving a total of 719 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cancer Research, 15 papers in Oncology and 14 papers in Pathology and Forensic Medicine. Recurrent topics in Brian J. Sworder's work include Cancer Genomics and Diagnostics (19 papers), Lymphoma Diagnosis and Treatment (14 papers) and CAR-T cell therapy research (11 papers). Brian J. Sworder is often cited by papers focused on Cancer Genomics and Diagnostics (19 papers), Lymphoma Diagnosis and Treatment (14 papers) and CAR-T cell therapy research (11 papers). Brian J. Sworder collaborates with scholars based in United States, Germany and India. Brian J. Sworder's co-authors include Ash A. Alizadeh, Maximilian Diehn, Chih Long Liu, Pamela Gehron Robey, Michael S. Khodadoust, David M. Kurtz, Lisa E. Wagar, Mark M. Davis, Ethan Fast and Joshua E. Elias and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Blood.

In The Last Decade

Brian J. Sworder

31 papers receiving 714 citations

Peers

Brian J. Sworder
Peet Nooijen Netherlands
Sreenivasulu Gunti United States
Julie Rytlewski United States
Yen Phung United States
Christopher Alvarez‐Breckenridge United States
Sylvia Herter Switzerland
Bingyan Wu United States
Dina Stroopinsky United States
Mark B. Geyer United States
Roberta Depenni Italy
Peet Nooijen Netherlands
Brian J. Sworder
Citations per year, relative to Brian J. Sworder Brian J. Sworder (= 1×) peers Peet Nooijen

Countries citing papers authored by Brian J. Sworder

Since Specialization
Citations

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

Fields of papers citing papers by Brian J. Sworder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brian J. Sworder

This figure shows the co-authorship network connecting the top 25 collaborators of Brian J. Sworder. A scholar is included among the top collaborators of Brian J. Sworder 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 Brian J. Sworder. Brian J. Sworder 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.
Roschewski, Mark, David M. Kurtz, Jason R. Westin, et al.. (2025). Remission Assessment by Circulating Tumor DNA in Large B-Cell Lymphoma. Journal of Clinical Oncology. 43(34). 3652–3661.
2.
Hosoya, Hitomi, Brian J. Sworder, Bita Sahaf, et al.. (2025). Deciphering response dynamics and treatment resistance from circulating tumor DNA after CAR T-cells in multiple myeloma. Nature Communications. 16(1). 1824–1824. 2 indexed citations
3.
Kastenschmidt, Jenna M., Joseph G. Schroers‐Martin, Brian J. Sworder, et al.. (2024). A human lymphoma organoid model for evaluating and targeting the follicular lymphoma tumor immune microenvironment. Cell stem cell. 31(3). 410–420.e4. 29 indexed citations
4.
Goldstein, Jordan, Florian Scherer, Brian J. Sworder, et al.. (2024). Anatomic Genetic Heterogeneity Is Uncovered By Concurrent Cell-Free DNA and Tissue Profiling and Predicts Treatment Resistance in Diffuse Large B-Cell Lymphoma. Blood. 144(Supplement 1). 4357–4357.
5.
Schroers‐Martin, Joseph G., Joanne Soo, Florian Scherer, et al.. (2023). Tracing Founder Mutations in Circulating and Tissue-Resident Follicular Lymphoma Precursors. Cancer Discovery. 13(6). 1310–1323. 23 indexed citations
6.
Gunaratne, Ruwan, Matthew Schwede, Matthew S. Alkaitis, et al.. (2023). Development of Circulating Tumor DNA (ctDNA) for Molecular Measurable Residual Disease (MRD) in Acute Myeloid Leukemia (AML). Blood. 142(Supplement 1). 4307–4307. 1 indexed citations
7.
Sworder, Brian J. & David M. Kurtz. (2023). Cell-free DNA in large B-cell lymphoma: MRD and beyond. Seminars in Hematology. 60(3). 142–149. 1 indexed citations
8.
Shukla, Navika, Joseph G. Schroers‐Martin, Brian J. Sworder, et al.. (2023). Specificity of immunoglobulin high-throughput sequencing minimal residual disease monitoring in non-Hodgkin lymphomas. Blood Advances. 8(3). 780–784. 3 indexed citations
9.
Steen, Chloé B., Bogdan Luca, Mohammad Shahrokh Esfahani, et al.. (2021). The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell. 39(10). 1422–1437.e10. 149 indexed citations
10.
Castro, Luis F de, Brian J. Sworder, Kathryn Futrega, et al.. (2021). Secreted frizzled related-protein 2 (Sfrp2) deficiency decreases adult skeletal stem cell function in mice. Bone Research. 9(1). 49–49. 15 indexed citations
11.
Kurtz, David M., Jacob J. Chabon, Brian J. Sworder, et al.. (2021). Leveraging phased variants for personalized minimal residual disease detection in localized non-small cell lung cancer.. Journal of Clinical Oncology. 39(15_suppl). 8518–8518. 2 indexed citations
12.
Esfahani, Mohammad Shahrokh, Stefan Alig, Mahya Mehrmohamadi, et al.. (2021). Noninvasive Cell-of-Origin Classification of Diffuse Large B-Cell Lymphoma Using Inferred Gene Expression from Cell-Free DNA Sequencing. Blood. 138(Supplement 1). 37–37.
13.
Schroers‐Martin, Joseph G., Joanne Soo, Gabriel Brisou, et al.. (2020). Recurrent Crebbp Mutations in Follicular Lymphoma Appear Localized to the Committed B-Cell Lineage. Blood. 136(Supplement 1). 30–31. 2 indexed citations
14.
Chen, Binbin, Michael S. Khodadoust, Niclas Olsson, et al.. (2019). Predicting HLA class II antigen presentation through integrated deep learning. Nature Biotechnology. 37(11). 1332–1343. 209 indexed citations
15.
Steen, Chloé B., Bogdan Luca, Mohammad Shahrokh Esfahani, et al.. (2019). An Atlas of Clinically-Distinct Tumor Cellular Ecosystems in Diffuse Large B Cell Lymphoma. Blood. 134(Supplement_1). 655–655. 3 indexed citations
16.
Sworder, Brian J., David M. Kurtz, Charles Macaulay, et al.. (2019). Circulating DNA for Molecular Response Prediction, Characterization of Resistance Mechanisms and Quantification of CAR T-Cells during Axicabtagene Ciloleucel Therapy. Blood. 134(Supplement_1). 550–550. 14 indexed citations
17.
Sanchorawala, Vaishali, David C. Seldin, Brian J. Sworder, et al.. (2015). Clinical presentation and treatment responses in IgM-related AL amyloidosis. Amyloid. 22(4). 229–235. 17 indexed citations
18.
Sworder, Brian J., Sayuri Yoshizawa, Prasun Mishra, et al.. (2015). Molecular profile of clonal strains of human skeletal stem/progenitor cells with different potencies. Stem Cell Research. 14(3). 297–306. 27 indexed citations
19.
Balakumaran, Arun, Prasun Mishra, Edyta Pawelczyk, et al.. (2014). Bone marrow skeletal stem/progenitor cell defects in dyskeratosis congenita and telomere biology disorders. Blood. 125(5). 793–802. 25 indexed citations
20.
Balakumaran, Arun, Edyta Pawelczyk, Jiaqiang Ren, et al.. (2010). Superparamagnetic Iron Oxide Nanoparticles Labeling of Bone Marrow Stromal (Mesenchymal) Cells Does Not Affect Their “Stemness”. PLoS ONE. 5(7). e11462–e11462. 83 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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