Chansik An

2.6k total citations
75 papers, 1.9k citations indexed

About

Chansik An is a scholar working on Radiology, Nuclear Medicine and Imaging, Hepatology and Epidemiology. According to data from OpenAlex, Chansik An has authored 75 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 34 papers in Hepatology and 26 papers in Epidemiology. Recurrent topics in Chansik An's work include Hepatocellular Carcinoma Treatment and Prognosis (34 papers), Liver Disease Diagnosis and Treatment (24 papers) and Radiomics and Machine Learning in Medical Imaging (20 papers). Chansik An is often cited by papers focused on Hepatocellular Carcinoma Treatment and Prognosis (34 papers), Liver Disease Diagnosis and Treatment (24 papers) and Radiomics and Machine Learning in Medical Imaging (20 papers). Chansik An collaborates with scholars based in South Korea, United States and Germany. Chansik An's co-authors include Myeong‐Jin Kim, Hyungjin Rhee, Young Nyun Park, Yong Eun Chung, Jin‐Young Choi, Kyunghwa Han, Yeun‐Yoon Kim, Sung Soo Ahn, Mi‐Suk Park and Seung‐Koo Lee and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Chansik An

70 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chansik An South Korea 26 951 928 658 374 314 75 1.9k
Mishal Mendiratta‐Lala United States 23 1.2k 1.3× 764 0.8× 702 1.1× 440 1.2× 382 1.2× 92 2.3k
Roman Kloeckner Germany 26 1.3k 1.3× 876 0.9× 620 0.9× 761 2.0× 596 1.9× 160 2.9k
Dawn Owen United States 27 803 0.8× 774 0.8× 291 0.4× 391 1.0× 736 2.3× 98 1.9k
Davide Ippolito Italy 24 441 0.5× 1.0k 1.1× 268 0.4× 332 0.9× 289 0.9× 125 1.9k
Cecilia Besa United States 25 994 1.0× 849 0.9× 846 1.3× 269 0.7× 273 0.9× 54 1.8k
Zahra Kassam Canada 20 553 0.6× 531 0.6× 253 0.4× 596 1.6× 812 2.6× 88 2.0k
Eun Sil Yu South Korea 23 1.1k 1.1× 481 0.5× 1.4k 2.1× 803 2.1× 352 1.1× 57 2.5k
Tonsok Kim Japan 34 1.7k 1.8× 1.5k 1.6× 1.2k 1.8× 830 2.2× 554 1.8× 108 3.5k
John D. Louie United States 27 775 0.8× 560 0.6× 349 0.5× 574 1.5× 535 1.7× 72 1.9k
Christoph Wald United States 24 423 0.4× 831 0.9× 315 0.5× 499 1.3× 564 1.8× 85 2.0k

Countries citing papers authored by Chansik An

Since Specialization
Citations

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

Fields of papers citing papers by Chansik An

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chansik An

This figure shows the co-authorship network connecting the top 25 collaborators of Chansik An. A scholar is included among the top collaborators of Chansik An 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 Chansik An. Chansik An 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
1.
2.
Rho, Seoung Yoon, Chansik An, Dai Hoon Han, et al.. (2024). Additional nodules detected using EOB-MRI in patients with resectable single hepatocellular carcinoma: an implication for active treatment strategy. SHILAP Revista de lepidopterología. 24(1). 92–101. 1 indexed citations
3.
An, Chansik, et al.. (2023). CT Evaluation of Long-Term Changes in Common Bile Duct Diameter after Cholecystectomy. PubMed. 85(3). 581–581.
4.
Park, Byung Kyu, et al.. (2023). Suspicious findings observed retrospectively on CT imaging performed before the diagnosis of pancreatic cancer. Journal of Gastrointestinal Oncology. 14(2). 1008–1018. 3 indexed citations
5.
Parakh, Anushri, Chansik An, Simon Lennartz, et al.. (2021). Recognizing and Minimizing Artifacts at Dual-Energy CT. Radiographics. 41(2). 509–523. 30 indexed citations
6.
Parakh, Anushri, Simon Lennartz, Chansik An, et al.. (2021). Dual-Energy CT Images: Pearls and Pitfalls. Radiographics. 41(1). 98–119. 77 indexed citations
7.
Bang, Minji, Chansik An, Yae Won Park, et al.. (2021). An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum. Translational Psychiatry. 11(1). 462–462. 26 indexed citations
8.
Sohn, Beomseok, Chansik An, Sung Soo Ahn, et al.. (2021). Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant. Journal of Neuro-Oncology. 155(3). 267–276. 20 indexed citations
9.
10.
Obmann, Markus M., Chansik An, Yuxin Sun, et al.. (2020). Improved Sensitivity and Reader Confidence in CT Colonography Using Dual-Layer Spectral CT: A Phantom Study. Radiology. 297(1). 99–107. 7 indexed citations
11.
An, Chansik, Hyunsun Lim, Dong Wook Kim, et al.. (2020). Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study. Scientific Reports. 10(1). 18716–18716. 137 indexed citations
12.
13.
Bae, Sohi, Chansik An, Sung Soo Ahn, et al.. (2020). Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Scientific Reports. 10(1). 12110–12110. 78 indexed citations
14.
Kim, Yeun‐Yoon, Jin‐Young Choi, Claude B. Sirlin, Chansik An, & Myeong‐Jin Kim. (2018). Pitfalls and problems to be solved in the diagnostic CT/MRI Liver Imaging Reporting and Data System (LI-RADS). European Radiology. 29(3). 1124–1132. 27 indexed citations
15.
An, Chansik, Do Young Kim, Jin‐Young Choi, et al.. (2018). Noncontrast magnetic resonance imaging versus ultrasonography for hepatocellular carcinoma surveillance (MIRACLE-HCC): study protocol for a prospective randomized trial. BMC Cancer. 18(1). 915–915. 32 indexed citations
16.
Rhee, Hyungjin, Myeong‐Jin Kim, Young Nyun Park, & Chansik An. (2018). A proposal of imaging classification of intrahepatic mass-forming cholangiocarcinoma into ductal and parenchymal types: clinicopathologic significance. European Radiology. 29(6). 3111–3121. 28 indexed citations
17.
Seo, Nieun, Yong Eun Chung, Chansik An, et al.. (2018). Feasibility of radiation dose reduction with iterative reconstruction in abdominopelvic CT for patients with inappropriate arm positioning. PLoS ONE. 13(12). e0209754–e0209754. 9 indexed citations
18.
Park, Chae Jung, Chansik An, Sumi Park, Jin‐Young Choi, & Myeong‐Jin Kim. (2017). Management of subcentimetre arterially enhancing and hepatobiliary hypointense lesions on gadoxetic acid-enhanced MRI in patients at risk for HCC. European Radiology. 28(4). 1476–1484. 11 indexed citations
19.
An, Chansik, Hyungjin Rhee, Kyunghwa Han, et al.. (2016). Added value of smooth hypointense rim in the hepatobiliary phase of gadoxetic acid-enhanced MRI in identifying tumour capsule and diagnosing hepatocellular carcinoma. European Radiology. 27(6). 2610–2618. 41 indexed citations
20.
An, Chansik, Kyunghwa Han, Myeong‐Jin Kim, et al.. (2015). Use of Preoperative MRI to Select Candidates for Local Excision of MRI-Staged T1 and T2 Rectal Cancer. Diseases of the Colon & Rectum. 58(10). 923–930. 6 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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