Matea Pavic
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Oncology
- Biomedical Engineering
- Radiation top 5%
- Co-authors
- Matthias GückenbergerStephanie Tanadini‐LangMarta BogowiczMartin W. HuellnerDiem VuongJohannes KraftGudrun TheileNicolaus Andratschke
- Topics
- Radiomics and Machine Learning in Medical Imaging (10 papers)Advanced Radiotherapy Techniques (7 papers)Pancreatic and Hepatic Oncology Research (6 papers)
- Journals
- Journal of Clinical OncologyClinical Cancer ResearchInternational Journal of Radiation Oncology*Biology*Physics
- Partner nations
- SwitzerlandItalyGermany
In The Last Decade
Matea Pavic
28 papers receiving 632 citations
Peers
Comparison fields: 5 of 72
- Radiology, Nuclear Medicine and Imaging 399
- Pulmonary and Respiratory Medicine 212
- Oncology 191
- Biomedical Engineering 118
- Radiation 107
Countries citing papers authored by Matea Pavic
This map shows the geographic impact of Matea Pavic'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 Matea Pavic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matea Pavic more than expected).
Fields of papers citing papers by Matea Pavic
This network shows the impact of papers produced by Matea Pavic. 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 Matea Pavic. The network helps show where Matea Pavic may publish in the future.
Co-authorship network of co-authors of Matea Pavic
This figure shows the co-authorship network connecting the top 25 collaborators of Matea Pavic. A scholar is included among the top collaborators of Matea Pavic 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 Matea Pavic. Matea Pavic is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 11 | |
| 5 | 5 | |
| 6 | 11 | |
| 7 | 3 | |
| 8 | 10 | |
| 9 | 16 | |
| 10 | 10 | |
| 11 | 69 | |
| 12 | 30 | |
| 13 | 5 | |
| 14 | 30 | |
| 15 | 41 | |
| 16 | 49 | |
| 17 | 48 | |
| 18 | 6 | |
| 19 | 1 | |
| 20 | 2 |
About Matea Pavic
Matea Pavic is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Medical Laboratory Technology, having authored 29 papers that have together received 634 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced Radiotherapy Techniques (7 papers) and Pancreatic and Hepatic Oncology Research (6 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (399 citations), Radiation (107 citations) and Health Informatics (14 citations). Matea Pavic has collaborated with scholars based in Switzerland, Italy and Germany. Frequent co-authors include Matthias Gückenberger, Stephanie Tanadini‐Lang, Marta Bogowicz, Martin W. Huellner, Diem Vuong, Johannes Kraft, Gudrun Theile, Nicolaus Andratschke, Gerhard Tröster and Oliver Riesterer. Their work appears in journals such as Journal of Clinical Oncology, Clinical Cancer Research and International Journal of Radiation Oncology*Biology*Physics.
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.