Yan Mee Law

1.6k total citations
40 papers, 995 citations indexed

About

Yan Mee Law is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Rheumatology. According to data from OpenAlex, Yan Mee Law has authored 40 papers receiving a total of 995 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Pulmonary and Respiratory Medicine, 20 papers in Radiology, Nuclear Medicine and Imaging and 14 papers in Rheumatology. Recurrent topics in Yan Mee Law's work include Prostate Cancer Diagnosis and Treatment (33 papers), Prostate Cancer Treatment and Research (21 papers) and Radiomics and Machine Learning in Medical Imaging (13 papers). Yan Mee Law is often cited by papers focused on Prostate Cancer Diagnosis and Treatment (33 papers), Prostate Cancer Treatment and Research (21 papers) and Radiomics and Machine Learning in Medical Imaging (13 papers). Yan Mee Law collaborates with scholars based in Singapore, United States and United Kingdom. Yan Mee Law's co-authors include Barış Türkbey, Peter L. Choyke, Peter A. Pinto, Joanna H. Shih, Maria J. Merino, Julia R. Fielding, Bradford J. Wood, Jamie Marko, Matthew D. Greer and Tristan Barrett and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiology and The Journal of Urology.

In The Last Decade

Yan Mee Law

39 papers receiving 989 citations

Peers

Yan Mee Law
Matthew D. Greer United States
Francesca Mertan United States
Frank‐Jan H. Drost Netherlands
Daniël F. Osses Netherlands
Amirhossein Mohammadian Bajgiran United States
Iztok Caglič United Kingdom
Samuel Borofsky United States
Ismail Kabakus United States
Morgan Pokorny Australia
Bashar Al‐Qaisieh United Kingdom
Matthew D. Greer United States
Yan Mee Law
Citations per year, relative to Yan Mee Law Yan Mee Law (= 1×) peers Matthew D. Greer

Countries citing papers authored by Yan Mee Law

Since Specialization
Citations

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

Fields of papers citing papers by Yan Mee Law

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yan Mee Law. 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 Yan Mee Law. The network helps show where Yan Mee Law may publish in the future.

Co-authorship network of co-authors of Yan Mee Law

This figure shows the co-authorship network connecting the top 25 collaborators of Yan Mee Law. A scholar is included among the top collaborators of Yan Mee Law 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 Yan Mee Law. Yan Mee Law 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
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Tay, Kae Jack, Enya H.W. Ong, Yu Guang Tan, et al.. (2024). 346P High genomic risk is associated with clinically significant prostate cancer (csPCa) recurrences following focal therapy (FT). Annals of Oncology. 35. S1535–S1535. 1 indexed citations
3.
Harmon, Stephanie A., Jesse Tetreault, Enis C. Yılmaz, et al.. (2024). Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification. Academic Radiology. 31(10). 4096–4106. 11 indexed citations
4.
Belue, Mason J., Enis C. Yılmaz, Yan Mee Law, et al.. (2024). Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI. Abdominal Radiology. 49(8). 2891–2901. 6 indexed citations
5.
Kenigsberg, Alexander P., Yan Mee Law, Enis C. Yılmaz, et al.. (2024). Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System. European Urology Open Science. 62. 74–80. 12 indexed citations
6.
7.
Belue, Mason J., Stephanie A. Harmon, Tristan Barrett, et al.. (2023). Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study. European Journal of Radiology. 170. 111259–111259. 6 indexed citations
8.
Png, Meng Ai, Lishya Liauw, Yan Mee Law, et al.. (2023). Evaluation of Compressed SENSE on Image Quality and Reduction of MRI Acquisition Time: A Clinical Validation Study. Academic Radiology. 31(3). 956–965. 3 indexed citations
9.
Lee, Alvin, Kenneth Chen, Christopher Cheng, et al.. (2023). Intensive sampling of the umbra and penumbra improves clinically significant prostate cancer detection and reduces risk of grade group upgrading at radical prostatectomy. World Journal of Urology. 41(8). 2265–2271. 3 indexed citations
10.
Belue, Mason J., Yan Mee Law, Jamie Marko, et al.. (2023). Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation. Academic Radiology. 31(4). 1429–1437. 9 indexed citations
11.
Tay, Kae Jack, Yu Guang Tan, J.S.P. Yuen, et al.. (2023). Surveillance one year post focal cryotherapy for clinically significant prostate cancer using mpMRI and PIRADS v2.1: An initial experience from a prospective phase II mandatory biopsy study. European Journal of Radiology Open. 11. 100529–100529. 4 indexed citations
12.
Yaow, Clyve Yu Leon, Seth En Teoh, Kae Jack Tay, et al.. (2023). Local Therapy on Clinically Lymph Node–positive Prostate Cancer: A Systematic Review and Meta-analysis. European Urology Oncology. 7(3). 355–364. 4 indexed citations
13.
Tan, Yu Guang, John Shyi Peng Yuen, Henry Ho, et al.. (2021). Key Steps in the Evaluation and Treatment Planning for Prostate Focal Cryotherapy. Videourology. 35(8). 1 indexed citations
14.
Cheng, Xin, et al.. (2021). Periprostatic schwannoma mimicking metastatic lymphadenopathy in a case of multifocal prostate adenocarcinoma. Journal of Radiology Case Reports. 15(3). 9–18. 1 indexed citations
15.
Chen, Kenneth, Yan Mee Law, Henry Ho, et al.. (2021). Robot-assisted Magnetic Resonance Imaging-ultrasound Fusion Transperineal Targeted Biopsy. Urology. 155. 46–46. 3 indexed citations
16.
Greer, Matthew D., Nathan Lay, Joanna H. Shih, et al.. (2018). Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. European Radiology. 28(10). 4407–4417. 73 indexed citations
17.
Valle, Luca, Matthew D. Greer, Joanna H. Shih, et al.. (2018). Multiparametric MRI for the detection of local recurrence of prostate cancer in the setting of biochemical recurrence after low dose rate brachytherapy. Diagnostic and Interventional Radiology. 24(1). 46–53. 21 indexed citations
18.
Law, Yan Mee, Puay Hoon Tan, John Carson Allen, et al.. (2018). Multiparametric MRI reporting using Prostate Imaging Reporting and Data System version 2.0 (PI-RADSv2) retains clinical efficacy in a predominantly post-biopsy patient population. Asian journal of urology. 6(3). 256–263. 3 indexed citations
19.
Greer, Matthew D., Joanna H. Shih, Nathan Lay, et al.. (2017). Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2. Radiology. 285(3). 859–869. 123 indexed citations
20.
Chen, Kenneth, Kae Jack Tay, Yan Mee Law, et al.. (2017). Outcomes of combination MRI-targeted and transperineal template biopsy in restaging low-risk prostate cancer for active surveillance. Asian journal of urology. 5(3). 184–193. 13 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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