Bum‐Sup Jang

1.0k total citations
67 papers, 663 citations indexed

About

Bum‐Sup Jang is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Bum‐Sup Jang has authored 67 papers receiving a total of 663 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Oncology, 22 papers in Pulmonary and Respiratory Medicine and 21 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Bum‐Sup Jang's work include Radiomics and Machine Learning in Medical Imaging (14 papers), Cancer Immunotherapy and Biomarkers (12 papers) and Breast Cancer Treatment Studies (11 papers). Bum‐Sup Jang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (14 papers), Cancer Immunotherapy and Biomarkers (12 papers) and Breast Cancer Treatment Studies (11 papers). Bum‐Sup Jang collaborates with scholars based in South Korea, United States and Ethiopia. Bum‐Sup Jang's co-authors include In Ah Kim, Ji Hyun Chang, Seung Hyuck Jeon, Il Han Kim, Kyung Hwan Shin, Won‐Sik Han, Hak Jae Kim, Eui Kyu Chie, Yu Jin Lim and Kyubo Kim and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Bioresource Technology.

In The Last Decade

Bum‐Sup Jang

60 papers receiving 658 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bum‐Sup Jang South Korea 14 261 221 147 133 125 67 663
Ji Hyun Chang South Korea 19 243 0.9× 361 1.6× 298 2.0× 201 1.5× 80 0.6× 85 1.1k
Rifaquat Rahman United States 17 333 1.3× 226 1.0× 243 1.7× 121 0.9× 562 4.5× 59 1000
Albert Assad United States 13 257 1.0× 250 1.1× 44 0.3× 152 1.1× 191 1.5× 43 826
Luís A. Pérez-Romasanta Spain 17 194 0.7× 138 0.6× 188 1.3× 103 0.8× 177 1.4× 64 731
Alfredo Zito Italy 14 248 1.0× 174 0.8× 92 0.6× 141 1.1× 28 0.2× 28 572
Seung Yeun Chung South Korea 14 194 0.7× 155 0.7× 159 1.1× 30 0.2× 25 0.2× 31 537
Indu Arun India 13 135 0.5× 233 1.1× 66 0.4× 112 0.8× 57 0.5× 51 597
Quchang Ouyang China 15 181 0.7× 718 3.2× 476 3.2× 178 1.3× 108 0.9× 80 1.0k
Zhe Jin China 14 283 1.1× 185 0.8× 161 1.1× 62 0.5× 27 0.2× 37 525
Xiaochun Meng China 18 648 2.5× 425 1.9× 262 1.8× 107 0.8× 25 0.2× 63 1.3k

Countries citing papers authored by Bum‐Sup Jang

Since Specialization
Citations

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

Fields of papers citing papers by Bum‐Sup Jang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bum‐Sup Jang. 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 Bum‐Sup Jang. The network helps show where Bum‐Sup Jang may publish in the future.

Co-authorship network of co-authors of Bum‐Sup Jang

This figure shows the co-authorship network connecting the top 25 collaborators of Bum‐Sup Jang. A scholar is included among the top collaborators of Bum‐Sup Jang 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 Bum‐Sup Jang. Bum‐Sup Jang 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.
Li, Xue, Tae‐Hoon Lee, Tae‐Min Kim, et al.. (2025). Distribution and failure patterns of primary central nervous system lymphoma related to the hippocampus: implications for hippocampal avoidance irradiation. Journal of Neuro-Oncology. 173(1). 95–104. 1 indexed citations
2.
Jang, Bum‐Sup, et al.. (2024). Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model. Cancers. 16(8). 1494–1494. 2 indexed citations
3.
Lee, Seungbok, et al.. (2024). B-Cell–Mediated Immunity Predicts Survival of Patients With Estrogen Receptor–Positive Breast Cancer. JCO Precision Oncology. 8(8). e2300263–e2300263. 1 indexed citations
4.
Jang, Bum‐Sup, et al.. (2024). Integrating Deep Learning–Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal Cancer. Cancer Research and Treatment. 57(1). 186–197. 1 indexed citations
5.
Jang, Bum‐Sup, et al.. (2024). Hype or Reality: Utility of Large Language Models in Radiation Oncology. International Journal of Radiation Oncology*Biology*Physics. 120(2). e629–e630.
6.
Shin, Kyung Hwan, et al.. (2024). Evaluation of cosmetic outcomes in breast reconstruction patients undergoing radiotherapy using an anomaly generative adversarial network model. Scientific Reports. 14(1). 15940–15940. 1 indexed citations
7.
Jang, Bum‐Sup, et al.. (2023). Development and benchmarking of a Korean audio speech recognition model for Clinician-Patient conversations in radiation oncology clinics. International Journal of Medical Informatics. 176. 105112–105112. 4 indexed citations
8.
Jeon, Seung Hyuck, Bum‐Sup Jang, Dong-Yun Kim, et al.. (2023). Dynamic Responses of Circulating T Cells After Stereotactic Body Radiation Therapy for Bone Metastasis in Patients With Breast Cancer. International Journal of Radiation Oncology*Biology*Physics. 118(3). 790–800. 5 indexed citations
9.
Shin, Kyung Hwan, et al.. (2023). Developing prompts from large language model for extracting clinical information from pathology and ultrasound reports in breast cancer. Radiation Oncology Journal. 41(3). 209–216. 67 indexed citations
10.
Jang, Bum‐Sup & Kyung Hwan Shin. (2022). Postmastectomy Radiation Therapy in Patients With Minimally Involved Lymph Nodes: A Review of the Current Data and Future Directions. Journal of Breast Cancer. 25(1). 1–1. 4 indexed citations
11.
Suh, Koung Jin, Se Hyun Kim, Yu Jung Kim, et al.. (2022). Clinical Application of Next-Generation Sequencing in Patients With Breast Cancer: Real-World Data. Journal of Breast Cancer. 25(5). 366–366. 10 indexed citations
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Jang, Bum‐Sup, et al.. (2020). Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07). International Journal of Radiation Oncology*Biology*Physics. 108(3). e742–e742. 1 indexed citations
16.
Jang, Bum‐Sup, Ji Hyun Chang, Eui Kyu Chie, et al.. (2020). Gut Microbiome Composition Is Associated with a Pathologic Response After Preoperative Chemoradiation in Patients with Rectal Cancer. International Journal of Radiation Oncology*Biology*Physics. 107(4). 736–746. 47 indexed citations
17.
Jang, Bum‐Sup, et al.. (2018). Prediction of Pseudoprogression Versus Progression Using Machine Learning Algorithm in Glioblastoma. International Journal of Radiation Oncology*Biology*Physics. 102(3). S61–S61. 2 indexed citations
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Jang, Bum‐Sup, Ji Hyun Chang, Sohee Oh, Yu Jin Lim, & Il Han Kim. (2017). Surgery vs. radiotherapy in patients with uveal melanoma. Strahlentherapie und Onkologie. 193(11). 931–942. 10 indexed citations
20.
Jang, Bum‐Sup, et al.. (2001). Preparation of 153Sm-Chitosan Complex for radiation synovectomy. Nuclear Medicine and Biology. 28(6). 719–725. 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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