Saewon Chun

529 total citations · 1 hit paper
8 papers, 155 citations indexed

About

Saewon Chun is a scholar working on Genetics, Neurology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Saewon Chun has authored 8 papers receiving a total of 155 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Genetics, 2 papers in Neurology and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Saewon Chun's work include Glioma Diagnosis and Treatment (5 papers), Neuroblastoma Research and Treatments (2 papers) and Cancer, Hypoxia, and Metabolism (2 papers). Saewon Chun is often cited by papers focused on Glioma Diagnosis and Treatment (5 papers), Neuroblastoma Research and Treatments (2 papers) and Cancer, Hypoxia, and Metabolism (2 papers). Saewon Chun collaborates with scholars based in United States, Egypt and Jordan. Saewon Chun's co-authors include Shuchi S. Pandya, Lori Steelman, Ingo K. Mellinghoff, Islam Hassan, Benjamin M. Ellingson, Patrick Y. Wen, Timothy F. Cloughesy, Isabel Arrillaga‐Romany, Anna M. Tassinari and Elizabeth A. Maher and has published in prestigious journals such as Nature Medicine, The FASEB Journal and Neuro-Oncology.

In The Last Decade

Saewon Chun

6 papers receiving 152 citations

Hit Papers

align trajectories

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.

Vorasidenib and ivosidenib in IDH1-mutant low-grade glioma: a randomized, perioperative phase 1 trial

2023 111 citations Ingo K. Mellinghoff, Min Lu et al. Nature Medicine profile →

Peers

Saewon Chun

GENET

CR

RNMI

MB

NEURO

Eike Steidl Germany

Ema Yoshioka Japan

Macarena I. de la Fuente United States

Alicia Steffens United States

Joo Yeon Nam United States

Shilpa S. Tummala United States

Maya Srikanth Graham United States

Susanna Ronchi Italy

Liana Nobre Canada

Kristyn Galbraith United States

Eike Steidl Germany

Saewon Chun

107 ×0.9

GENET

24 ×0.5

CR

83 ×2.1

RNMI

19 ×0.5

MB

20 ×0.6

NEURO

Citations per year, relative to Saewon Chun Saewon Chun (= 1×) peers Eike Steidl

Countries citing papers authored by Saewon Chun

Since Specialization

Citations

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

Fields of papers citing papers by Saewon Chun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saewon Chun

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Ellingson, Benjamin M., Robert Chong, Ida Sonni, et al.. (2025). A comparative study of preclinical and clinical molecular imaging response to EGFR inhibition using osimertinib in glioblastoma. Neuro-Oncology Advances. 7(1). vdaf022–vdaf022.

2.

Chun, Saewon, et al.. (2025). Art in Anatomy session as a method of formative feedback in preclerkship medical education. AJP Advances in Physiology Education. 49(1). 210–218. 1 indexed citations

3.

Cho, Nicholas S., Vien Le, Francesco Sanvito, et al.. (2024). Digital “flipbooks” for enhanced visual assessment of simple and complex brain tumors. Neuro-Oncology. 26(10). 1823–1836. 1 indexed citations

4.

Mellinghoff, Ingo K., Min Lu, Patrick Y. Wen, et al.. (2023). Vorasidenib and ivosidenib in IDH1-mutant low-grade glioma: a randomized, perioperative phase 1 trial. Nature Medicine. 29(3). 615–622. 111 indexed citations breakdown →

5.

Chun, Saewon, et al.. (2022). Art in Anatomy for Interactive Hands‐on Learning: A Descriptive Study. The FASEB Journal. 36(S1). 1 indexed citations

6.

Hagiwara, Akifumi, Jingwen Yao, Catalina Raymond, et al.. (2021). “Aerobic glycolytic imaging” of human gliomas using combined pH-, oxygen-, and perfusion-weighted magnetic resonance imaging. NeuroImage Clinical. 32. 102882–102882. 8 indexed citations

7.

Ellingson, Benjamin M., Grace Hyun J. Kim, Jihey Lee, et al.. (2021). Volumetric measurements are preferred in the evaluation of mutant IDH inhibition in non-enhancing diffuse gliomas: Evidence from a phase I trial of ivosidenib. Neuro-Oncology. 24(5). 770–778. 31 indexed citations

8.

Cloughesy, Timothy F., Benjamin M. Ellingson, Saewon Chun, et al.. (2021). CTNI-14. EVALUATING METABOLIC ALTERATIONS IN PATIENTS WITH EGFR ACTIVATED RECURRENT GLIOBLASTOMA (RGBM) BY INHIBITING EGFR WITH OSIMERTINIB. Neuro-Oncology. 23(Supplement_6). vi62–vi62. 2 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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