Ken Kudura

896 total citations
40 papers, 592 citations indexed

About

Ken Kudura is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ken Kudura has authored 40 papers receiving a total of 592 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Oncology and 13 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ken Kudura's work include Cancer Immunotherapy and Biomarkers (11 papers), Medical Imaging Techniques and Applications (9 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Ken Kudura is often cited by papers focused on Cancer Immunotherapy and Biomarkers (11 papers), Medical Imaging Techniques and Applications (9 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Ken Kudura collaborates with scholars based in Switzerland, Germany and Austria. Ken Kudura's co-authors include Martin W. Huellner, Philipp A. Kaufmann, Michael Messerli, Dominik C. Benz, Aju P. Pazhenkottil, Cathérine Gebhard, Irene A. Burger, Ronny R. Buechel, Tobias A. Fuchs and Lucas Basler and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Ken Kudura

38 papers receiving 585 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ken Kudura Switzerland 14 340 163 156 153 88 40 592
Fabiana Gregucci Italy 17 197 0.6× 178 1.1× 287 1.8× 33 0.2× 68 0.8× 46 631
Guangjie Yang China 14 389 1.1× 102 0.6× 241 1.5× 113 0.7× 100 1.1× 47 575
Ari Chong South Korea 16 420 1.2× 163 1.0× 269 1.7× 68 0.4× 181 2.1× 59 843
Siavash Mehdizadeh Seraj United States 12 264 0.8× 140 0.9× 153 1.0× 35 0.2× 61 0.7× 47 495
Mahdi Zirakchian Zadeh United States 13 286 0.8× 167 1.0× 142 0.9× 40 0.3× 65 0.7× 55 549
Devaki Shilpa Surasi United States 11 191 0.6× 166 1.0× 199 1.3× 39 0.3× 128 1.5× 48 568
Derfel ap Dafydd United Kingdom 8 213 0.6× 118 0.7× 155 1.0× 62 0.4× 95 1.1× 25 410
Jaap Zindler Netherlands 20 205 0.6× 288 1.8× 682 4.4× 68 0.4× 143 1.6× 43 898
Stephen M. Smith United States 10 129 0.4× 99 0.6× 80 0.5× 26 0.2× 79 0.9× 34 362
Igor Sirák Czechia 15 165 0.5× 191 1.2× 204 1.3× 38 0.2× 263 3.0× 74 699

Countries citing papers authored by Ken Kudura

Since Specialization
Citations

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

Fields of papers citing papers by Ken Kudura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ken Kudura

This figure shows the co-authorship network connecting the top 25 collaborators of Ken Kudura. A scholar is included among the top collaborators of Ken Kudura 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 Ken Kudura. Ken Kudura 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.
Kudura, Ken, Arnoud J. Templeton, Tobias Zellweger, et al.. (2025). Tumor Segmentation on PSMA PET/CT Predicts Survival in Biochemical Recurrence of Prostate Cancer: A Retrospective Study Using [68Ga]Ga-PSMA-11 and [18F]-PSMA-1007. Cancers. 17(13). 2249–2249. 1 indexed citations
2.
Kudura, Ken, Kris Denhaerynck, Daniel C. Steinemann, et al.. (2024). Diagnostic accuracy and treatment benefit of PET/CT in staging of colorectal cancer compared to conventional imaging. Surgical Oncology. 57. 102151–102151. 3 indexed citations
4.
Kudura, Ken, et al.. (2023). Predictive Value of Total Metabolic Tumor Burden Prior to Treatment in NSCLC Patients Treated with Immune Checkpoint Inhibition. Journal of Clinical Medicine. 12(11). 3725–3725. 4 indexed citations
6.
Kudura, Ken, Arnoud J. Templeton, Martin Hoffmann, et al.. (2023). An Innovative Non-Linear Prediction Model for Clinical Benefit in Women with Newly Diagnosed Breast Cancer Using Baseline FDG-PET/CT and Clinical Data. Cancers. 15(22). 5476–5476. 1 indexed citations
7.
Skawran, Stephan, Michael Messerli, Fotis A. Kotasidis, et al.. (2022). Can Dynamic Whole-Body FDG PET Imaging Differentiate between Malignant and Inflammatory Lesions?. Life. 12(9). 1350–1350. 12 indexed citations
8.
Kudura, Ken, Béatrice Kern, Martin Hoffmann, & Kwadwo Antwi. (2022). The use of 18F-Choline-PET/CT in chronic lymphocytic leukaemia. Journal of Clinical Images and Medical Case Reports. 3(4). 1 indexed citations
9.
Liberini, Virginia, Michael Messerli, Lars Husmann, et al.. (2021). Improved detection of in-transit metastases of malignant melanoma with BSREM reconstruction in digital [18F]FDG PET/CT. European Radiology. 31(10). 8011–8020. 13 indexed citations
11.
Dimitriou, Florentia, Peter K. H. Lau, Prachi Bhave, et al.. (2021). Real-life data for first-line combination immune-checkpoint inhibition and targeted therapy in patients with melanoma brain metastases. European Journal of Cancer. 156. 149–163. 11 indexed citations
12.
Husmann, Lars, Urs J. Muehlematter, Felix Grimm, et al.. (2021). PET/CT helps to determine treatment duration in patients with resected as well as inoperable alveolar echinococcosis. Parasitology International. 83. 102356–102356. 12 indexed citations
13.
Basler, Lucas, Hubert S. Gabryś, Sabrina A. Hogan, et al.. (2020). Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition. Clinical Cancer Research. 26(16). 4414–4425. 69 indexed citations
14.
Muehlematter, Urs J., Gaspar Delso, Daniela A. Ferraro, et al.. (2020). 68Ga-PSMA-11 dose reduction for dedicated pelvic imaging with simultaneous PET/MR using TOF BSREM reconstructions. European Radiology. 30(6). 3188–3197. 10 indexed citations
15.
Mihic‐Probst, Daniela, Michael Reinehr, Isabel Kolm, et al.. (2020). The role of macrophages type 2 and T-regs in immune checkpoint inhibitor related adverse events. Immunobiology. 225(5). 152009–152009. 22 indexed citations
16.
Benz, Dominik C., Georgios Benetos, Georgios Rampidis, et al.. (2020). Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy. Journal of cardiovascular computed tomography. 14(5). 444–451. 128 indexed citations
19.
Schwyzer, Moritz, Katharina Martini, Dominik C. Benz, et al.. (2019). Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance. European Radiology. 30(4). 2031–2040. 43 indexed citations
20.
Giannopoulos, Andreas A., Michael Messerli, Moritz Schwyzer, et al.. (2018). Ultra-low-dose computed tomography for attenuation correction of cadmium-zinc-telluride single photon emission computed tomography myocardial perfusion imaging. Journal of Nuclear Cardiology. 27(1). 228–237. 6 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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