An Tang

17.3k total citations · 4 hit papers
246 papers, 10.4k citations indexed

About

An Tang is a scholar working on Epidemiology, Radiology, Nuclear Medicine and Imaging and Hepatology. According to data from OpenAlex, An Tang has authored 246 papers receiving a total of 10.4k indexed citations (citations by other indexed papers that have themselves been cited), including 99 papers in Epidemiology, 90 papers in Radiology, Nuclear Medicine and Imaging and 79 papers in Hepatology. Recurrent topics in An Tang's work include Liver Disease Diagnosis and Treatment (93 papers), Hepatocellular Carcinoma Treatment and Prognosis (70 papers) and Radiomics and Machine Learning in Medical Imaging (36 papers). An Tang is often cited by papers focused on Liver Disease Diagnosis and Treatment (93 papers), Hepatocellular Carcinoma Treatment and Prognosis (70 papers) and Radiomics and Machine Learning in Medical Imaging (36 papers). An Tang collaborates with scholars based in Canada, United States and China. An Tang's co-authors include Claude B. Sirlin, Guy Cloutier, Samuel Kadoury, Gabriel Chartrand, Victoria Chernyak, Aya Kamaya, Mustafa R. Bashir, Simon Turcotte, Eugene Vorontsov and Kathryn J. Fowler and has published in prestigious journals such as Journal of Clinical Investigation, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

An Tang

237 papers receiving 10.2k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning: A Primer for Radiologists

2017 788 citations Eugene Vorontsov, Simon Turcotte et al. Radiographics profile →
Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients

2018 736 citations Victoria Chernyak, Kathryn J. Fowler et al. profile →
Nonalcoholic Fatty Liver Disease: MR Imaging of Liver Proton Density Fat Fraction to Assess Hepatic Steatosis

2013 433 citations An Tang, Claude B. Sirlin et al. profile →
Epidemiology of hepatocellular carcinoma: target population for surveillance and diagnosis

2017 343 citations An Tang, Victoria Chernyak et al. Abdominal Radiology profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
An Tang	4.1k	3.9k	3.8k	1.3k	1.2k	246	10.4k
Seong Ho Park	3.5k 0.8×	2.6k 0.7×	3.8k 1.0×	1.1k 0.8×	3.9k 3.4×	345	13.2k
Christoph F. Dietrich	3.6k 0.9×	3.1k 0.8×	4.1k 1.1×	2.1k 1.6×	4.0k 3.5×	490	12.8k
Kyung Won Kim	2.1k 0.5×	1.9k 0.5×	2.8k 0.7×	845 0.7×	3.0k 2.6×	415	10.5k
Jens Ricke	1.9k 0.5×	3.7k 1.0×	3.0k 0.8×	1.4k 1.1×	2.8k 2.4×	525	10.8k
Chuansheng Zheng	891 0.2×	1.0k 0.3×	3.5k 0.9×	972 0.8×	729 0.6×	345	9.8k
Philippe Soyer	1.6k 0.4×	1.6k 0.4×	2.3k 0.6×	401 0.3×	3.5k 3.0×	371	9.0k
Steven S. Raman	1.8k 0.4×	2.8k 0.7×	3.1k 0.8×	1.1k 0.9×	2.0k 1.7×	268	9.5k
Luca Saba	2.4k 0.6×	323 0.1×	3.8k 1.0×	1.2k 0.9×	1.8k 1.6×	580	11.8k
Bernd Hamm	2.8k 0.7×	2.2k 0.6×	9.5k 2.5×	4.1k 3.2×	5.3k 4.5×	905	22.0k
Kyunghwa Han	1.2k 0.3×	497 0.1×	3.9k 1.0×	940 0.7×	1.7k 1.4×	328	8.0k

Countries citing papers authored by An Tang

Since Specialization

Citations

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

Fields of papers citing papers by An Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of An Tang

This figure shows the co-authorship network connecting the top 25 collaborators of An Tang. A scholar is included among the top collaborators of An Tang 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 An Tang. An Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lefebvre, Thierry, Merve Kulbay, Guillaume Gilbert, et al.. (2025). Prospective Comparison of DWI ‐Derived Virtual MR Elastography and Conventional MR Elastography in Metabolic Dysfunction‐Associated Steatotic Liver Disease and Healthy Volunteers. Journal of Magnetic Resonance Imaging. 63(4). 996–1008.

Wilson, Mitchell P., Gavin Low, Alexandra Medellin, et al.. (2025). Part 3: CAR Metabolic Dysfunction-Associated Steatotic Liver Disease Working Group Recommendations for Ultrasound Shear Wave Elastography and MR Elastography Program Implementation, Funding, and Quality Assurance. Canadian Association of Radiologists Journal. 77(1). 85–97. 2 indexed citations

Wilson, Mitchell P., Gavin Low, Abdel Aziz Shaheen, et al.. (2025). Part 2: CAR Metabolic Dysfunction-Associated Steatotic Liver Disease Working Group Recommendations for Risk Stratifying Patients With MASLD. Canadian Association of Radiologists Journal. 77(1). 73–84. 2 indexed citations

Thériault-Lauzier, Pascal, Olivier Tastet, Bahareh Taji, et al.. (2024). A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform. Canadian Journal of Cardiology. 40(10). 1828–1840. 17 indexed citations

Zhang, Haoming, Zhang Hongsong, Nan Wang, et al.. (2024). Phase compositions and thermophysical performances for (Sm1-xYbx)3TaO7 compounds. Ceramics International. 50(11). 18576–18583. 3 indexed citations

Xu, Jin, et al.. (2024). Changes in the Biology and Susceptibility of Weevil (Cylas formicarius) to the Insecticide Spinetoram as a Response to Cadmium Contamination. Toxics. 12(4). 304–304. 2 indexed citations

Lefebvre, Thierry, Merve Kulbay, Emmanuel Montagnon, et al.. (2024). Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers.. Magnetic Resonance Imaging. 113. 110223–110223.

Trudel, Dominique, et al.. (2024). Cell-Level GNN-Based Prediction of Tumor Regression Grade in Colorectal Liver Metastases From Histopathology Images. PolyPublie (École Polytechnique de Montréal). 1–5. 1 indexed citations

Zhang, Quan, Simon Lessard, C. Ng, et al.. (2024). Human-scale navigation of magnetic microrobots in hepatic arteries. Science Robotics. 9(87). eadh8702–eadh8702. 25 indexed citations

10.

Brady, Adrian P., Bibb Allen, Jaron Chong, et al.. (2024). Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA. Radiology Artificial Intelligence. 6(1). e230513–e230513. 26 indexed citations

11.

Bilodeau, Laurent, Catherine Huët, Assia Belblidia, et al.. (2023). IL-6 Trans-Signaling Is Increased in Diabetes, Impacted by Glucolipotoxicity, and Associated With Liver Stiffness and Fibrosis in Fatty Liver Disease. Diabetes. 72(12). 1820–1834. 14 indexed citations

12.

Marks, Robert M., Alice Fung, Irene Cruite, et al.. (2023). The adoption of LI-RADS: a survey of non-academic radiologists. Abdominal Radiology. 48(8). 2514–2524. 6 indexed citations

13.

Messaoudi, Nouredin, Rolando Rebolledo, Emmanuel Montagnon, et al.. (2023). Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases. Journal of Translational Medicine. 21(1). 507–507. 13 indexed citations

14.

Fetzer, David T., Theodore T. Pierce, Michelle L. Robbin, et al.. (2023). US Quantification of Liver Fat: Past, Present, and Future. Radiographics. 43(7). e220178–e220178. 19 indexed citations

15.

Mullie, Louis, Jonathan Afilalo, Patrick Archambault, et al.. (2023). CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data. Journal of the American Medical Informatics Association. 31(3). 651–665. 8 indexed citations

16.

Fowler, Kathryn J., Mustafa R. Bashir, David T. Fetzer, et al.. (2022). Universal Liver Imaging Lexicon: Imaging Atlas for Research and Clinical Practice. Radiographics. 43(1). e220066–e220066. 12 indexed citations

17.

Cunha, Guilherme Moura, Kathryn J. Fowler, Bachir Taouli, et al.. (2021). How to Use LI-RADS to Report Liver CT and MRI Observations. Radiographics. 41(5). 1352–1367. 20 indexed citations

18.

Yang, Fang, et al.. (2020). Phoenixin 14 Inhibits High-Fat Diet-Induced Non-Alcoholic Fatty Liver Disease in Experimental Mice. SHILAP Revista de lepidopterología. 2 indexed citations

19.

Vorontsov, Eugene, Milena Cerny, Philippe Régnier, et al.. (2019). Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases. Radiology Artificial Intelligence. 1(2). 180014–180014. 95 indexed citations

20.

Lü, Na, et al.. (2015). Hepatocyte-Specific Ablation of PP2A Catalytic SubunitαAttenuates Liver Fibrosis Progression via TGF-β1/Smad Signaling. BioMed Research International. 2015. 1–10. 20 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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