Natascha Rieder

608 total citations
17 papers, 359 citations indexed

About

Natascha Rieder is a scholar working on Oncology, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Natascha Rieder has authored 17 papers receiving a total of 359 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Oncology, 4 papers in Molecular Biology and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Natascha Rieder's work include Cancer Immunotherapy and Biomarkers (4 papers), CAR-T cell therapy research (3 papers) and Peptidase Inhibition and Analysis (3 papers). Natascha Rieder is often cited by papers focused on Cancer Immunotherapy and Biomarkers (4 papers), CAR-T cell therapy research (3 papers) and Peptidase Inhibition and Analysis (3 papers). Natascha Rieder collaborates with scholars based in Switzerland, Germany and France. Natascha Rieder's co-authors include Catherine Ibisch, Delphine Loussouarn, Frédérique Nguyen, L. Peña, Mario Campone, Adelina Gama, Jérôme Abadie, Anton Belousov, Thomas Friess and Christian Klein and has published in prestigious journals such as PLoS ONE, Cancer Research and Scientific Reports.

In The Last Decade

Natascha Rieder

16 papers receiving 348 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Natascha Rieder Switzerland 8 166 149 97 85 74 17 359
Doo Hyun Chung South Korea 8 318 1.9× 245 1.6× 216 2.2× 115 1.4× 14 0.2× 9 624
Emily A. Merkel United States 11 33 0.2× 244 1.6× 145 1.5× 53 0.6× 32 0.4× 17 340
Sapna M. Amin United States 10 57 0.3× 181 1.2× 100 1.0× 34 0.4× 29 0.4× 22 302
Bryan Gammon United States 10 21 0.1× 176 1.2× 120 1.2× 75 0.9× 17 0.2× 17 321
Scott R. Dalton United States 9 29 0.2× 363 2.4× 203 2.1× 59 0.7× 45 0.6× 23 561
Yukiko Teramoto Japan 10 64 0.4× 406 2.7× 156 1.6× 102 1.2× 9 0.1× 37 514
Kyriakos Chatzopoulos Greece 11 100 0.6× 298 2.0× 135 1.4× 143 1.7× 4 0.1× 40 491
Candice Perry United States 8 64 0.4× 103 0.7× 169 1.7× 144 1.7× 5 0.1× 12 361
Neil Rajan United Kingdom 15 46 0.3× 269 1.8× 198 2.0× 30 0.4× 9 0.1× 75 582
K. Montone United States 6 44 0.3× 167 1.1× 106 1.1× 168 2.0× 11 0.1× 11 331

Countries citing papers authored by Natascha Rieder

Since Specialization
Citations

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

Fields of papers citing papers by Natascha Rieder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Natascha Rieder. 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 Natascha Rieder. The network helps show where Natascha Rieder may publish in the future.

Co-authorship network of co-authors of Natascha Rieder

This figure shows the co-authorship network connecting the top 25 collaborators of Natascha Rieder. A scholar is included among the top collaborators of Natascha Rieder 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 Natascha Rieder. Natascha Rieder is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Reis, Bernardo Sgarbi, Daniel J. Lee, Stefan N. Symeonides, et al.. (2024). 161P Fibroblast activation protein (FAP)-CD40 (RO7300490) mediates intratumoral DC maturation and modulation of the tumor microenvironment. Annals of Oncology. 35. S279–S280. 2 indexed citations
2.
Reis, Bernhard, et al.. (2024). Tumor beta2-microglobulin and HLA-A expression is increased by immunotherapy and can predict response to CIT in association with other biomarkers. Frontiers in Immunology. 15. 1285049–1285049. 7 indexed citations
3.
Dejardin, David, Anton Kraxner, Annika Blank, et al.. (2023). A Composite Decision Rule of CD8+ T-cell Density in Tumor Biopsies Predicts Efficacy in Early-stage, Immunotherapy Trials. Clinical Cancer Research. 30(4). 877–882. 4 indexed citations
4.
Cannarile, Michael A., Vaios Karanikas, Bernhard Reis, et al.. (2023). Facts and Hopes on Biomarkers for Successful Early Clinical Immunotherapy Trials: Innovative Patient Enrichment Strategies. Clinical Cancer Research. 30(8). 1448–1456. 1 indexed citations
5.
Bissinger, Stefan, Carina Hage, Verena Brand, et al.. (2021). Macrophage depletion induces edema through release of matrix-degrading proteases and proteoglycan deposition. Science Translational Medicine. 13(598). 29 indexed citations
6.
Hage, Carina, Sabine Hoves, Natascha Rieder, et al.. (2019). Characterizing responsive and refractory orthotopic mouse models of hepatocellular carcinoma in cancer immunotherapy. PLoS ONE. 14(7). e0219517–e0219517. 17 indexed citations
7.
Schelhaas, Sonja, Sven Hermann, Natascha Rieder, et al.. (2018). Thymidine Metabolism as a Confounding Factor for 3′-Deoxy-3′-18F-Fluorothymidine Uptake After Therapy in a Colorectal Cancer Model. Journal of Nuclear Medicine. 59(7). 1063–1069. 4 indexed citations
8.
Kollmorgen, Gwendlyn, Stefan Scheiblich, Fabian Birzele, et al.. (2017). A re-engineered immunotoxin shows promising preclinical activity in ovarian cancer. Scientific Reports. 7(1). 18086–18086. 10 indexed citations
9.
Nguyen, Frédérique, L. Peña, Catherine Ibisch, et al.. (2017). Canine invasive mammary carcinomas as models of human breast cancer. Part 1: natural history and prognostic factors. Breast Cancer Research and Treatment. 167(3). 635–648. 99 indexed citations
10.
Abadie, Jérôme, Frédérique Nguyen, Delphine Loussouarn, et al.. (2017). Canine invasive mammary carcinomas as models of human breast cancer. Part 2: immunophenotypes and prognostic significance. Breast Cancer Research and Treatment. 167(2). 459–468. 84 indexed citations
11.
12.
Zając, Magdalena, Thomas Friess, Natascha Rieder, et al.. (2015). Evaluation of Protein Profiles From Treated Xenograft Tumor Models Identifies an Antibody Panel for Formalin-fixed and Paraffin-embedded (FFPE) Tissue Analysis by Reverse Phase Protein Arrays (RPPA). Molecular & Cellular Proteomics. 14(10). 2775–2785. 7 indexed citations
13.
Kollmorgen, Gwendlyn, Gerhard Niederfellner, Alexander Lifke, et al.. (2013). Antibody mediated CDCP1 degradation as mode of action for cancer targeted therapy. Molecular Oncology. 7(6). 1142–1151. 26 indexed citations
14.
Herting, Frank, Thomas Friess, Gunter Muth, et al.. (2013). Enhanced anti-tumor activity of the glycoengineered type II CD20 antibody obinutuzumab (GA101) in combination with chemotherapy in xenograft models of human lymphoma. Leukemia & lymphoma. 55(9). 2151–5160. 39 indexed citations
15.
Nguyen, Frédérique, Jérôme Abadie, Delphine Loussouarn, et al.. (2011). PD08-10: High Frequency of Triple Negative Mammary Carcinomas in the Dog as Model of Human Breast Cancer.. Cancer Research. 71(24_Supplement). PD08–10.
16.
Wartha, Katharina, Rebecca Croasdale, Ulrich Brinkmann, et al.. (2011). Abstract LB-212: XGFR, an Fc-engineered dual signaling inhibitor targeting IGF-1R and EGFR. Cancer Research. 71(8_Supplement). LB–212. 1 indexed citations
17.
Neumeister, Alexander, et al.. (1994). [Fall/winter depression and its therapy].. PubMed. 106(21). 665–70. 2 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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