King Tan

4.3k total citations
31 papers, 1.5k citations indexed

About

King Tan is a scholar working on Pathology and Forensic Medicine, Oncology and Genetics. According to data from OpenAlex, King Tan has authored 31 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Pathology and Forensic Medicine, 9 papers in Oncology and 8 papers in Genetics. Recurrent topics in King Tan's work include Lymphoma Diagnosis and Treatment (22 papers), CNS Lymphoma Diagnosis and Treatment (7 papers) and Chronic Lymphocytic Leukemia Research (6 papers). King Tan is often cited by papers focused on Lymphoma Diagnosis and Treatment (22 papers), CNS Lymphoma Diagnosis and Treatment (7 papers) and Chronic Lymphocytic Leukemia Research (6 papers). King Tan collaborates with scholars based in Canada, United States and Australia. King Tan's co-authors include Randy D. Gascoyne, Christian Steidl, Joseph M. Connors, David W. Scott, Susana Ben‐Neriah, Robert Kridel, Laurie H. Sehn, Graham W. Slack, Adèle Telenius and Fong Chun Chan and has published in prestigious journals such as Journal of Clinical Oncology, Blood and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

King Tan

31 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
King Tan Canada 15 1.1k 812 427 333 327 31 1.5k
Daisuke Ennishi Japan 20 958 0.9× 656 0.8× 452 1.1× 253 0.8× 263 0.8× 109 1.5k
Maria Calaminici United Kingdom 19 1.3k 1.2× 985 1.2× 547 1.3× 639 1.9× 209 0.6× 44 1.8k
Robert Kridel Canada 21 1.4k 1.2× 1.0k 1.3× 593 1.4× 412 1.2× 302 0.9× 81 2.0k
Clémentine Sarkozy France 20 1.0k 0.9× 791 1.0× 371 0.9× 203 0.6× 219 0.7× 73 1.4k
Diane C. Louie United States 19 1.4k 1.3× 944 1.2× 666 1.6× 330 1.0× 363 1.1× 24 2.0k
Sergio Cogliatti Switzerland 23 1.5k 1.4× 1.1k 1.4× 796 1.9× 390 1.2× 317 1.0× 60 2.2k
Sandrine Roulland France 16 1.1k 0.9× 715 0.9× 506 1.2× 342 1.0× 135 0.4× 43 1.5k
Jörg Kalla Germany 25 1.6k 1.5× 1.0k 1.2× 723 1.7× 269 0.8× 215 0.7× 40 2.0k
Robert Coupland Canada 22 844 0.8× 683 0.8× 477 1.1× 241 0.7× 166 0.5× 40 1.7k
Enrico Derenzini Italy 25 1.2k 1.1× 766 0.9× 344 0.8× 370 1.1× 269 0.8× 99 1.9k

Countries citing papers authored by King Tan

Since Specialization
Citations

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

Fields of papers citing papers by King Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of King Tan

This figure shows the co-authorship network connecting the top 25 collaborators of King Tan. A scholar is included among the top collaborators of King Tan 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 King Tan. King Tan 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.
Khan, Tayyaba, Luke St Heaps, Mark Dexter, et al.. (2021). FISH analysis of brain smears obtained at intraoperative diagnosis – An accurate and fast method to detect 1p/19q-codeletion in gliomas. Journal of Clinical Neuroscience. 92. 115–119. 1 indexed citations
2.
Tang, Mónica, Rachel O’Connell, Frédéric Amant, et al.. (2019). PARAGON: A Phase II study of anastrozole in patients with estrogen receptor-positive recurrent/metastatic low-grade ovarian cancers and serous borderline ovarian tumors. Gynecologic Oncology. 154(3). 531–538. 54 indexed citations
3.
Villa, Diego, King Tan, Christian Steidl, et al.. (2019). Molecular features of a large cohort of primary central nervous system lymphoma using tissue microarray. Blood Advances. 3(23). 3953–3961. 21 indexed citations
4.
Tan, King, et al.. (2019). Antemortem histopathology and imaging findings in a case of Marchiafava-Bignami disease. Journal of Clinical Neuroscience. 66. 273–275. 8 indexed citations
5.
Savage, Kerry J., Graham W. Slack, Anja Mottok, et al.. (2016). Impact of dual expression of MYC and BCL2 by immunohistochemistry on the risk of CNS relapse in DLBCL. Blood. 127(18). 2182–2188. 123 indexed citations
6.
Twa, David D. W., Anja Mottok, Fong Chun Chan, et al.. (2015). Recurrent genomic rearrangements in primary testicular lymphoma. The Journal of Pathology. 236(2). 136–141. 41 indexed citations
7.
Twa, David D. W., Fong Chun Chan, Susana Ben‐Neriah, et al.. (2014). Genomic rearrangements involving programmed death ligands are recurrent in primary mediastinal large B-cell lymphoma. Blood. 123(13). 2062–2065. 231 indexed citations
8.
Savage, Kerry J., Laurie H. Sehn, Diego Villa, et al.. (2014). The Impact of Concurrent MYC BCL2 Protein Expression on the Risk of Secondary Central Nervous System Relapse in Diffuse Large B-Cell Lymphoma (DLBCL). Blood. 124(21). 495–495. 5 indexed citations
9.
Hamilton, Sarah, Elaine S. Wai, King Tan, et al.. (2013). Treatment and Outcomes in Patients With Primary Cutaneous B-Cell Lymphoma: The BC Cancer Agency Experience. International Journal of Radiation Oncology*Biology*Physics. 87(4). 719–725. 50 indexed citations
10.
Kanakry, Jennifer A., Hailun Li, Lan L. Gellert, et al.. (2013). Plasma Epstein-Barr virus DNA predicts outcome in advanced Hodgkin lymphoma: correlative analysis from a large North American cooperative group trial. Blood. 121(18). 3547–3553. 103 indexed citations
11.
Scott, David W., Karen Mungall, Susana Ben‐Neriah, et al.. (2012). TBL1XR1/TP63: a novel recurrent gene fusion in B-cell non-Hodgkin lymphoma. Blood. 119(21). 4949–4952. 43 indexed citations
12.
Kridel, Robert, King Tan, Abdulwahab J. Al-Tourah, et al.. (2012). Tumor-Associated Macrophages Predict Outcome in Follicular Lymphoma. Blood. 120(21). 682–682. 1 indexed citations
13.
Jankova, Lucy, Graham Robertson, Charles Chan, et al.. (2012). Glutathione S-transferase Pi expression predicts response to adjuvant chemotherapy for stage C colon cancer: a matched historical control study. BMC Cancer. 12(1). 196–196. 10 indexed citations
14.
Tan, King, David W. Scott, Fangxin Hong, et al.. (2012). Tumor-associated macrophages predict inferior outcomes in classic Hodgkin lymphoma: a correlative study from the E2496 Intergroup trial. Blood. 120(16). 3280–3287. 158 indexed citations
15.
16.
Villa, Diego, Tamara Shenkier, Laurie H. Sehn, et al.. (2011). Diffuse Large B-Cell Lymphoma with Testicular Involvement: Outcome and Risk of CNS Relapse in the Rituximab Era. Blood. 118(21). 780–780. 11 indexed citations
17.
18.
Kridel, Robert, Barbara Meissner, Sanja Rogić, et al.. (2011). Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma. Blood. 119(9). 1963–1971. 241 indexed citations
19.
Carroll, Susan, et al.. (2009). Radiation Recall Dermatitis After Pre-Sensitization With Pegylated Liposomal Doxorubicin. Cancer Investigation. 27(4). 397–401. 6 indexed citations
20.
Liu, Jason, et al.. (2002). Lock-free scheduling of logical processes in parallel simulation. 22–31. 8 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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