Kay See Tan
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- Lung Cancer Diagnosis and Treatment 54
- Lung Cancer Treatments and Mutations 41
- Gastric Cancer Management and Outcomes 22
- Medical Imaging and Pathology Studies 11
- Oncology top 2%
- Otorhinolaryngology top 5%
- Surgery top 2%
- Esophageal Cancer Research and Treatment 29
- Esophageal and GI Pathology 23
- Cancer Research top 5%
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- Cardiac, Anesthesia and Surgical Outcomes 22
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- Radiomics and Machine Learning in Medical Imaging 13
- Co-authors
- Prasad S. AdusumilliDavid R. JonesTakashi EguchiWilliam D. TravisMatthew J. BottDaniela MolenaManjit S. BainsDaniel F. Heitjan
- Journals
- Journal of Clinical Oncology (3 papers)SHILAP Revista de lepidopterología (3 papers)Molecular Cell (1 paper)
- Partner nations
- United StatesJapanChina
In The Last Decade
Kay See Tan
174 papers receiving 3.9k citations
Peers
Comparison fields: 5 of 127
- Pulmonary and Respiratory Medicine 2.3k
- Oncology 1.3k
- Otorhinolaryngology 129
- Surgery 1.1k
- Cancer Research 373
Countries citing papers authored by Kay See Tan
This map shows the geographic impact of Kay See Tan'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 Kay See Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kay See Tan more than expected).
Fields of papers citing papers by Kay See Tan
This network shows the impact of papers produced by Kay See Tan. 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 Kay See Tan. The network helps show where Kay See Tan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kay See Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 2 | |
| 7 | 2023 | 11 | |
| 8 | 2023 | 3 | |
| 9 | 2022 | 2 | |
| 10 | 2022 | 59 | |
| 11 | 2022 | 0 | |
| 12 | 2021 | 6 | |
| 13 | 2021 | 20 | |
| 14 | 2020 | 6 | |
| 15 | 2019 | 67 | |
| 16 | 2019 | 3 | |
| 17 | 2019 | 50 | |
| 18 | 2018 | 40 | |
| 19 | 2017 | 56 | |
| 20 | 2014 | 280 |
About Kay See Tan
Kay See Tan is a scholar working on Pulmonary and Respiratory Medicine, Critical Care and Intensive Care Medicine and Anesthesiology and Pain Medicine, having authored 186 papers that have together received 4.0k indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (54 papers), Lung Cancer Treatments and Mutations (41 papers), Esophageal Cancer Research and Treatment (29 papers), Esophageal and GI Pathology (23 papers), Gastric Cancer Management and Outcomes (22 papers), Cardiac, Anesthesia and Surgical Outcomes (22 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Medical Imaging and Pathology Studies (11 papers). The work is most often cited by research in Pulmonary and Respiratory Medicine (2.3k citations), Oncology (1.3k citations) and Otorhinolaryngology (129 citations). Kay See Tan has collaborated with scholars based in United States, Japan and China. Frequent co-authors include Prasad S. Adusumilli, David R. Jones, Takashi Eguchi, William D. Travis, Matthew J. Bott, Daniela Molena, Manjit S. Bains, Daniel F. Heitjan, Natasha Rekhtman and Valerie W. Rusch. Their work appears in journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Molecular Cell.
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.