Vincent Lam

6.1k total citations
148 papers, 2.9k citations indexed

About

Vincent Lam is a scholar working on Surgery, Oncology and Hepatology. According to data from OpenAlex, Vincent Lam has authored 148 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Surgery, 52 papers in Oncology and 49 papers in Hepatology. Recurrent topics in Vincent Lam's work include Hepatocellular Carcinoma Treatment and Prognosis (42 papers), Cholangiocarcinoma and Gallbladder Cancer Studies (28 papers) and Pancreatic and Hepatic Oncology Research (27 papers). Vincent Lam is often cited by papers focused on Hepatocellular Carcinoma Treatment and Prognosis (42 papers), Cholangiocarcinoma and Gallbladder Cancer Studies (28 papers) and Pancreatic and Hepatic Oncology Research (27 papers). Vincent Lam collaborates with scholars based in Australia, United States and Canada. Vincent Lam's co-authors include Henry Pleass, Jerome Laurence, A. J. Richardson, Michael Hollands, Emma Johnston, Tony Pang, Lawrence Yuen, Wayne J. Hawthorne, Ahmer Hameed and Manish I. Patel and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Annals of Surgery.

In The Last Decade

Vincent Lam

132 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vincent Lam Australia 32 1.4k 1.1k 1.0k 731 450 148 2.9k
Dieter C. Bröering Germany 36 3.0k 2.2× 911 0.9× 1.9k 1.9× 701 1.0× 595 1.3× 141 4.0k
Nicholas N. Nissen United States 31 1.0k 0.7× 977 0.9× 904 0.9× 380 0.5× 1.4k 3.0× 128 3.2k
Mariana Berho United States 34 1.9k 1.4× 1.3k 1.3× 1.5k 1.5× 451 0.6× 1.9k 4.3× 116 5.1k
A. Encke Germany 28 1.0k 0.7× 706 0.7× 714 0.7× 567 0.8× 365 0.8× 182 2.7k
Matthias Biebl Germany 27 1.3k 1.0× 457 0.4× 287 0.3× 874 1.2× 280 0.6× 155 2.3k
Gi‐Won Song South Korea 36 3.9k 2.9× 1.2k 1.1× 3.5k 3.5× 945 1.3× 1.5k 3.4× 374 5.7k
Matthew H. Levine United States 30 872 0.6× 456 0.4× 499 0.5× 263 0.4× 373 0.8× 93 3.6k
Luciano De Carlis Italy 38 3.1k 2.2× 717 0.7× 3.7k 3.6× 509 0.7× 1.6k 3.6× 234 5.1k
Hooman Yarmohammadi United States 25 449 0.3× 607 0.6× 777 0.8× 469 0.6× 382 0.8× 135 2.1k
G. Anton Decker Belgium 30 2.7k 2.0× 735 0.7× 217 0.2× 1.9k 2.6× 273 0.6× 65 3.6k

Countries citing papers authored by Vincent Lam

Since Specialization
Citations

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

Fields of papers citing papers by Vincent Lam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vincent Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Vincent Lam. A scholar is included among the top collaborators of Vincent Lam 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 Vincent Lam. Vincent Lam 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.
Akabane, Miho, Jun Kawashima, Selamawit Woldesenbet, et al.. (2025). Preoperative diagnostic failure in gallbladder cancer: Influence of tumor location and size on imaging precision. European Journal of Surgical Oncology. 51(7). 109979–109979.
2.
3.
Catalano, Giovanni, Laura Alaimo, Andrea Ruzzenente, et al.. (2025). Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning. HPB. 27(6). 807–815.
4.
Munir, Muhammad Musaab, Yutaka Endo, Muhammad Muntazir Mehdi Khan, et al.. (2024). Development of an artificial intelligence–based model to predict early recurrence of neuroendocrine liver metastasis after resection. Journal of Gastrointestinal Surgery. 28(11). 1828–1837. 2 indexed citations
5.
Catalano, Giovanni, Laura Alaimo, Andrea Ruzzenente, et al.. (2024). Machine learning prediction of early recurrence after surgery for gallbladder cancer. British journal of surgery. 111(11). 4 indexed citations
6.
Nagrial, Adnan, Mark Wong, Siobhán O’Neill, et al.. (2024). Systematic Review of Preoperative Prognostic Biomarkers in Perihilar Cholangiocarcinoma. Cancers. 16(4). 698–698. 3 indexed citations
7.
Liao, Hongen, et al.. (2023). Cascade Multi-Level Transformer Network for Surgical Workflow Analysis. IEEE Transactions on Medical Imaging. 42(10). 2817–2831. 7 indexed citations
8.
9.
Pang, Tony, et al.. (2022). Neutrophil‐lymphocyte ratio and platelet‐lymphocyte ratio use in detecting bowel ischaemia in adhesional small bowel obstruction. ANZ Journal of Surgery. 92(11). 2915–2920. 9 indexed citations
10.
11.
Lam, Vincent, Keiko Hino, Daniel Ory, et al.. (2021). IP 3 R-driven increases in mitochondrial Ca 2+ promote neuronal death in NPC disease. Proceedings of the National Academy of Sciences. 118(40). 26 indexed citations
12.
Wei, Tao, Xu‐Feng Zhang, Fabio Bagante, et al.. (2020). Early Versus Late Recurrence of Hepatocellular Carcinoma After Surgical Resection Based on Post-recurrence Survival: an International Multi-institutional Analysis. Journal of Gastrointestinal Surgery. 25(1). 125–133. 49 indexed citations
13.
Laurence, Jerome, Ahmer Hameed, Richard D. Allen, et al.. (2020). How to do it: a robotic kidney autotransplant. ANZ Journal of Surgery. 90(7-8). 1472–1473. 6 indexed citations
14.
Pang, Tony, Lawrence Yuen, Vincent Lam, et al.. (2020). Day‐only elective cholecystectomy: early experience and barriers to implementation in Australia. ANZ Journal of Surgery. 91(4). 590–596. 4 indexed citations
15.
Ma, Mingyang, Shaohui Mei, Shuai Wan, et al.. (2020). Keyframe Extraction From Laparoscopic Videos via Diverse and Weighted Dictionary Selection. IEEE Journal of Biomedical and Health Informatics. 25(5). 1686–1698. 18 indexed citations
16.
Zhou, Gang, Shuanglin Han, Zhiqiang Zhang, et al.. (2020). An aptamer-based drug delivery agent (CD133-apt-Dox) selectively and effectively kills liver cancer stem-like cells. Cancer Letters. 501. 124–132. 54 indexed citations
17.
Pang, Tony, Anthony J. Gill, Ross C. Smith, et al.. (2016). A pre-operative clinical model to predict microvascular invasion and long-term outcome after resection of hepatocellular cancer: The Australian experience. European Journal of Surgical Oncology. 42(10). 1576–1583. 36 indexed citations
18.
Wi, Hun, Alistair McEwan, Vincent Lam, et al.. (2015). Real‐time conductivity imaging of temperature and tissue property changes during radiofrequency ablation: An ex vivo model using weighted frequency difference. Bioelectromagnetics. 36(4). 277–286. 17 indexed citations
19.
Wilson, George S., Zenan Hu, Wei Duan, et al.. (2013). Efficacy of Using Cancer Stem Cell Markers in Isolating and Characterizing Liver Cancer Stem Cells. Stem Cells and Development. 22(19). 2655–2664. 39 indexed citations
20.
Lam, Vincent, Claire Taylor, Jerome Laurence, et al.. (2008). Heart allograft acceptance induced by anti-CD3 antibody in high-responder rats: Effect on foxp3 and cytokine expression and graft infiltration. Transplant Immunology. 19(1). 20–24. 4 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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