Brian Letzen

1.0k total citations · 1 hit paper
17 papers, 752 citations indexed

About

Brian Letzen is a scholar working on Radiology, Nuclear Medicine and Imaging, Hepatology and Molecular Biology. According to data from OpenAlex, Brian Letzen has authored 17 papers receiving a total of 752 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Hepatology and 3 papers in Molecular Biology. Recurrent topics in Brian Letzen's work include Hepatocellular Carcinoma Treatment and Prognosis (9 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and MRI in cancer diagnosis (4 papers). Brian Letzen is often cited by papers focused on Hepatocellular Carcinoma Treatment and Prognosis (9 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and MRI in cancer diagnosis (4 papers). Brian Letzen collaborates with scholars based in United States, Germany and Austria. Brian Letzen's co-authors include Julius Chapiro, Todd Schlachter, Clinton J. Wang, MingDe Lin, Lynn Jeanette Savic, Isabel Schobert, James S. Duncan, Charlie Alexander Hamm, Marc Ferrante and Jeffrey C. Weinreb and has published in prestigious journals such as PLoS ONE, Scientific Reports and PROTEOMICS.

In The Last Decade

Brian Letzen

17 papers receiving 736 citations

Hit Papers

Deep learning for liver tumor diagnosis part I: developme... 2019 2026 2021 2023 2019 50 100 150 200
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.

  1. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
    2019 245 citations Charlie Alexander Hamm, Clinton J. Wang et al. European Radiology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Brian Letzen United States 11 367 233 157 146 104 17 752
Martino Bosco Italy 19 91 0.2× 33 0.1× 123 0.8× 142 1.0× 29 0.3× 34 825
Yuwei Xia China 17 749 2.0× 113 0.5× 110 0.7× 78 0.5× 225 2.2× 72 994
Xiaozhu Lin China 19 567 1.5× 50 0.2× 111 0.7× 52 0.4× 290 2.8× 72 922
Zeyan Xu China 13 440 1.2× 32 0.1× 301 1.9× 36 0.2× 69 0.7× 42 758
S Chaudhari United States 9 516 1.4× 11 0.0× 64 0.4× 36 0.2× 122 1.2× 16 692

Countries citing papers authored by Brian Letzen

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Brian Letzen

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

All Works

17 of 17 papers shown
1.
Savic, Lynn Jeanette, Julius Chapiro, Brian Letzen, et al.. (2022). Response assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization. Clinical Imaging. 89. 112–119. 2 indexed citations
2.
Fereydooni, Arash, Evan M. Chen, Brian Letzen, et al.. (2021). Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy. European Radiology. 31(12). 8858–8867. 5 indexed citations
3.
Letzen, Brian, Rohil Malpani, Milena Miszczuk, et al.. (2021). Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial. Clinical Imaging. 78. 194–200. 12 indexed citations
4.
Bousabarah, Khaled, Brian Letzen, Lynn Jeanette Savic, et al.. (2020). Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology. 46(1). 216–225. 64 indexed citations
5.
Savic, Lynn Jeanette, Brian Letzen, Isabel Schobert, et al.. (2020). Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer. Scientific Reports. 10(1). 18026–18026. 10 indexed citations
6.
Hamm, Charlie Alexander, Clinton J. Wang, Lynn Jeanette Savic, et al.. (2019). Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European Radiology. 29(7). 3338–3347. 245 indexed citations breakdown →
7.
Wang, Clinton J., Charlie Alexander Hamm, Lynn Jeanette Savic, et al.. (2019). Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. European Radiology. 29(7). 3348–3357. 122 indexed citations
8.
Wang, Clinton J., Charlie Alexander Hamm, Brian Letzen, & James S. Duncan. (2019). A probabilistic approach for interpretable deep learning in liver cancer diagnosis. 29–29. 3 indexed citations
9.
Letzen, Brian, Clinton J. Wang, & Julius Chapiro. (2018). The Role of Artificial Intelligence in Interventional Oncology: A Primer. Journal of Vascular and Interventional Radiology. 30(1). 38–41.e1. 28 indexed citations
10.
Fereydooni, Arash, Brian Letzen, Milena Miszczuk, et al.. (2018). Irinotecan-Eluting 75–150-μm Embolics Lobar Chemoembolization in Patients with Colorectal Cancer Liver Metastases: A Prospective Single-Center Phase I Study. Journal of Vascular and Interventional Radiology. 29(12). 1646–1653.e5. 13 indexed citations
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Letzen, Brian, et al.. (2016). Intra-arterial therapies for liver cancer: assessing tumor response. Expert Review of Anticancer Therapy. 17(2). 119–127. 13 indexed citations
14.
Letzen, Brian, Charles Matouk, & Pramod Bonde. (2014). Particle Embolization for the Treatment of Life-Threatening Epistaxis in a Left Ventricular Assist Device Patient. ASAIO Journal. 61(1). 102–103. 2 indexed citations
15.
Chaerkady, Raghothama, Brian Letzen, Santosh Renuse, et al.. (2011). Quantitative temporal proteomic analysis of human embryonic stem cell differentiation into oligodendrocyte progenitor cells. PROTEOMICS. 11(20). 4007–4020. 37 indexed citations
16.
Letzen, Brian, et al.. (2010). MicroRNA Expression Profiling of Oligodendrocyte Differentiation from Human Embryonic Stem Cells. PLoS ONE. 5(5). e10480–e10480. 97 indexed citations
17.
Kerr, Candace L., Brian Letzen, Christine Hill, et al.. (2010). Efficient Differentiation of Human Embryonic Stem Cells into Oligodendrocyte Progenitors for Application in a Rat Contusion Model of Spinal Cord Injury. International Journal of Neuroscience. 120(4). 305–313. 83 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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