Brian Letzen
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Hepatology top 5%
- Hepatocellular Carcinoma Treatment and Prognosis
Papers in
-
- Hepatocellular Carcinoma Treatment and Prognosis 9
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- Radiomics and Machine Learning in Medical Imaging 4
- MRI in cancer diagnosis 2
- Co-authors
- Julius Chapiro (11 shared papers)Clinton J. Wang (4 shared papers)Todd Schlachter (6 shared papers)MingDe Lin (7 shared papers)Isabel Schobert (4 shared papers)Lynn Jeanette Savic (5 shared papers)James S. Duncan (4 shared papers)Charlie Alexander Hamm (3 shared papers)
- Journals
- Journal of Vascular and Interventional Radiology (3 papers)European Radiology (3 papers)ASAIO Journal (2 papers)Expert Review of Anticancer Therapy (1 paper)PROTEOMICS (1 paper)
- Partner nations
- United StatesGermanyNetherlands
In The Last Decade
Brian Letzen
17 papers receiving 778 citations
Brian Letzen's Hit Papers
Peers
Comparison fields: 5 of 69
- Health Informatics 62
- Hepatology 195
- Developmental Neuroscience 79
- Radiology, Nuclear Medicine and Imaging 281
- Health Information Management 31
Countries citing papers authored by Brian Letzen
This map shows the geographic impact of Brian Letzen'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 Brian Letzen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Letzen more than expected).
Fields of papers citing papers by Brian Letzen
This network shows the impact of papers produced by Brian Letzen. 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 Brian Letzen. The network helps show where Brian Letzen may publish in the future.
Co-authors
The 25 scholars most cited alongside Brian Letzen, 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 | Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI Hit paper breakdown → | 2019 | 262 |
| 2 | 2019 | 126 | |
| 3 | 2010 | 100 | |
| 4 | 2010 | 87 | |
| 5 | 2020 | 66 | |
| 6 | 2011 | 37 | |
| 7 | 2018 | 36 | |
| 8 | 2017 | 15 | |
| 9 | 2021 | 14 | |
| 10 | 2018 | 13 | |
| 11 | 2016 | 13 | |
| 12 | 2020 | 10 | |
| 13 | 2021 | 4 | |
| 14 | 2019 | 4 | |
| 15 | 2022 | 2 | |
| 16 | 2014 | 2 | |
| 17 | 2017 | 1 |
About Brian Letzen
Brian Letzen is a scholar working on Hepatology, Radiology, Nuclear Medicine and Imaging, Molecular Biology, Genetics and Artificial Intelligence, having authored 17 papers that have together received 792 indexed citations. Recurring topics across this work include Hepatocellular Carcinoma Treatment and Prognosis (9 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Neurogenesis and neuroplasticity mechanisms (2 papers), MRI in cancer diagnosis (2 papers), Pluripotent Stem Cells Research (2 papers), AI in cancer detection (2 papers), Epigenetics and DNA Methylation (1 paper) and Artificial Intelligence in Healthcare (1 paper). The work is most often cited by research in Health Informatics (62 citations), Hepatology (195 citations), Developmental Neuroscience (79 citations), Radiology, Nuclear Medicine and Imaging (281 citations) and Health Information Management (31 citations). Brian Letzen has collaborated with scholars based in United States, Germany and Netherlands. Frequent co-authors include Julius Chapiro, Clinton J. Wang, Todd Schlachter, MingDe Lin, Isabel Schobert, Lynn Jeanette Savic, James S. Duncan, Charlie Alexander Hamm, Marc Ferrante and Jeffrey C. Weinreb. Their work appears in journals such as Journal of Vascular and Interventional Radiology, European Radiology, ASAIO Journal, Expert Review of Anticancer Therapy and PROTEOMICS.
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.