Jennifer M. Munson

3.7k total citations
54 papers, 1.6k citations indexed

About

Jennifer M. Munson is a scholar working on Oncology, Biomedical Engineering and Genetics. According to data from OpenAlex, Jennifer M. Munson has authored 54 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Oncology, 18 papers in Biomedical Engineering and 17 papers in Genetics. Recurrent topics in Jennifer M. Munson's work include Glioma Diagnosis and Treatment (17 papers), 3D Printing in Biomedical Research (15 papers) and Cancer Cells and Metastasis (12 papers). Jennifer M. Munson is often cited by papers focused on Glioma Diagnosis and Treatment (17 papers), 3D Printing in Biomedical Research (15 papers) and Cancer Cells and Metastasis (12 papers). Jennifer M. Munson collaborates with scholars based in United States, Switzerland and Netherlands. Jennifer M. Munson's co-authors include Adrian C. Shieh, Shayn M. Peirce, Ravi V. Bellamkonda, Bruce A. Corliss, Mohammad S. Azimi, Walter L. Murfee, Melody A. Swartz, Alexandra R. Harris, Kathryn M. Kingsmore and Rebecca R. Pompano and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer Research and Advanced Drug Delivery Reviews.

In The Last Decade

Jennifer M. Munson

50 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jennifer M. Munson United States 19 604 513 482 240 239 54 1.6k
Manja Wobus Germany 23 484 0.8× 326 0.6× 864 1.8× 288 1.2× 420 1.8× 68 2.1k
Cynthia Hajal United States 15 816 1.4× 475 0.9× 484 1.0× 178 0.7× 164 0.7× 20 1.6k
Mara Gilardi United States 19 838 1.4× 799 1.6× 623 1.3× 331 1.4× 215 0.9× 33 1.9k
Uri Weinberg Switzerland 18 676 1.1× 311 0.6× 494 1.0× 135 0.6× 115 0.5× 114 1.8k
José M. Ayuso United States 26 1.0k 1.7× 710 1.4× 473 1.0× 231 1.0× 156 0.7× 55 1.7k
Barbara Hempstead United States 10 770 1.3× 345 0.7× 867 1.8× 151 0.6× 172 0.7× 12 2.1k
Maria‐Magdalena Georgescu United States 24 471 0.8× 503 1.0× 1.4k 2.9× 157 0.7× 393 1.6× 34 2.6k
Giorgio Seano United States 24 613 1.0× 915 1.8× 1.2k 2.4× 410 1.7× 515 2.2× 36 2.6k
Gregory H. Underhill United States 23 1.2k 2.1× 331 0.6× 692 1.4× 474 2.0× 414 1.7× 50 2.6k
Monique M.A. Verstegen Netherlands 31 477 0.8× 677 1.3× 1.0k 2.1× 292 1.2× 199 0.8× 87 2.8k

Countries citing papers authored by Jennifer M. Munson

Since Specialization
Citations

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

Fields of papers citing papers by Jennifer M. Munson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jennifer M. Munson. 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 Jennifer M. Munson. The network helps show where Jennifer M. Munson may publish in the future.

Co-authorship network of co-authors of Jennifer M. Munson

This figure shows the co-authorship network connecting the top 25 collaborators of Jennifer M. Munson. A scholar is included among the top collaborators of Jennifer M. Munson 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 Jennifer M. Munson. Jennifer M. Munson 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.
Munson, Jennifer M., et al.. (2025). Ex Vivo Model of Breast Cancer Cell Invasion in Live Lymph Node Tissue. ACS Pharmacology & Translational Science. 8(3). 690–705. 1 indexed citations
2.
Gutova, Margarita, et al.. (2024). Model discovery approach enables noninvasive measurement of intra-tumoral fluid transport in dynamic MRI. APL Bioengineering. 8(2). 26106–26106. 1 indexed citations
3.
Gutova, Margarita, Bihong T. Chen, Mark S. Shiroishi, et al.. (2024). Structural and practical identifiability of contrast transport models for DCE-MRI. PLoS Computational Biology. 20(5). e1012106–e1012106. 4 indexed citations
4.
Michelhaugh, Sharon K. & Jennifer M. Munson. (2023). TMIC-21. SPHINGOSINE-1-PHOSPHATE RECEPTOR 3 (S1PR3) IN THE GLIOBLASTOMA TUMOR MICROENVIRONMENT MAY PLAY A ROLE IN IMMUNOSUPPRESSION. Neuro-Oncology. 25(Supplement_5). v282–v282.
5.
Munson, Jennifer M., et al.. (2023). Development of a Synthetic, Injectable Hydrogel to Capture Residual Glioblastoma and Glioblastoma Stem‐Like Cells with CXCL12‐Mediated Chemotaxis. Advanced Healthcare Materials. 12(14). e2300671–e2300671. 9 indexed citations
6.
Munson, Jennifer M., et al.. (2023). Modeling lymphangiogenesis: Pairing in vitro and in vivo metrics. Microcirculation. 30(2-3). e12802–e12802. 8 indexed citations
7.
8.
Pompano, Rebecca R., et al.. (2022). Engineering in vitro immune-competent tissue models for testing and evaluation of therapeutics. Advanced Drug Delivery Reviews. 182. 114111–114111. 22 indexed citations
9.
Cornelison, R. Chase, Samantha C. Schwager, Daniela Cimini, et al.. (2022). A patient-designed tissue-engineered model of the infiltrative glioblastoma microenvironment. npj Precision Oncology. 6(1). 54–54. 14 indexed citations
10.
Munson, Jennifer M., et al.. (2022). Autologous Gradient Formation under Differential Interstitial Fluid Flow Environments. SHILAP Revista de lepidopterología. 2(1). 16–33. 3 indexed citations
11.
12.
Sahoo, Prativa, Yujie Cui, Bihong T. Chen, et al.. (2021). Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma. Neuro-Oncology Advances. 3(1). vdab174–vdab174. 8 indexed citations
13.
Gutova, Margarita, et al.. (2021). Delivery strategies for cell-based therapies in the brain: overcoming multiple barriers. Drug Delivery and Translational Research. 11(6). 2448–2467. 12 indexed citations
14.
Brooks, Elizabeth, et al.. (2019). Applicability of drug response metrics for cancer studies using biomaterials. Philosophical Transactions of the Royal Society B Biological Sciences. 374(1779). 20180226–20180226. 43 indexed citations
15.
Munson, Jennifer M., et al.. (2019). Methods to measure, model and manipulate fluid flow in brain. Journal of Neuroscience Methods. 333. 108541–108541. 19 indexed citations
16.
Munson, Jennifer M., et al.. (2019). Convection-Enhanced Delivery: Connection to and Impact of Interstitial Fluid Flow. Frontiers in Oncology. 9. 966–966. 78 indexed citations
17.
Kingsmore, Kathryn M., Andrea Vaccari, Daniel Abler, et al.. (2018). MRI analysis to map interstitial flow in the brain tumor microenvironment. APL Bioengineering. 2(3). 54 indexed citations
18.
Munson, Jennifer M., et al.. (2017). Chemoprotection Across the Tumor Border: Cancer Cell Response to Doxorubicin Depends on Stromal Fibroblast Ratios and Interstitial Therapeutic Transport. Cellular and Molecular Bioengineering. 10(5). 463–481. 11 indexed citations
19.
Munson, Jennifer M., et al.. (2013). Evans blue nanocarriers visually demarcate margins of invasive gliomas. Drug Delivery and Translational Research. 5(2). 116–124. 11 indexed citations
20.
Munson, Jennifer M., Ravi V. Bellamkonda, & Melody A. Swartz. (2012). Interstitial Flow in a 3D Microenvironment Increases Glioma Invasion by a CXCR4-Dependent Mechanism. Cancer Research. 73(5). 1536–1546. 139 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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