Sangwon Shin

1.9k total citations
16 papers, 132 citations indexed

About

Sangwon Shin is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Sangwon Shin has authored 16 papers receiving a total of 132 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 6 papers in Pulmonary and Respiratory Medicine and 6 papers in Artificial Intelligence. Recurrent topics in Sangwon Shin's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and Colorectal Cancer Treatments and Studies (3 papers). Sangwon Shin is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and Colorectal Cancer Treatments and Studies (3 papers). Sangwon Shin collaborates with scholars based in South Korea, United States and Japan. Sangwon Shin's co-authors include Soo Ick Cho, Jong Seok Ahn, Seokhwi Kim, Su‐A Yang, Eun Kyung Park, Chan‐Young Ock, Ki Hwan Kim, Hojun Kim, Sujin Kim and Jung Hun Kang and has published in prestigious journals such as Journal of Clinical Oncology, Annals of Oncology and IEEE Access.

In The Last Decade

Sangwon Shin

12 papers receiving 128 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sangwon Shin South Korea	4	50	49	42	35	29	16	132
Maximilian Alber Germany	5	77 1.5×	30 0.6×	52 1.2×	17 0.5×	18 0.6×	13	159
David L. Hölscher Germany	6	51 1.0×	16 0.3×	65 1.5×	32 0.9×	36 1.2×	11	154
Mustafa Nasir-Moin United States	5	63 1.3×	29 0.6×	62 1.5×	23 0.7×	12 0.4×	12	118
Roman D. Buelow Germany	7	109 2.2×	47 1.0×	85 2.0×	15 0.4×	20 0.7×	8	192
Keluo Yao United States	8	83 1.7×	25 0.5×	52 1.2×	22 0.6×	9 0.3×	14	135
Mateo Sokač Denmark	6	34 0.7×	43 0.9×	40 1.0×	15 0.4×	36 1.2×	18	150
Jack Zeineh United States	5	50 1.0×	20 0.4×	33 0.8×	30 0.9×	14 0.5×	14	103
Didem Çifçi Germany	5	103 2.1×	45 0.9×	113 2.7×	18 0.5×	15 0.5×	10	167
Georg Wölflein United Kingdom	6	81 1.6×	18 0.4×	47 1.1×	16 0.5×	22 0.8×	10	155
Omar S. M. El Nahhas Germany	7	92 1.8×	34 0.7×	75 1.8×	17 0.5×	26 0.9×	11	177

Countries citing papers authored by Sangwon Shin

Since Specialization

Citations

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

Fields of papers citing papers by Sangwon Shin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sangwon Shin

This figure shows the co-authorship network connecting the top 25 collaborators of Sangwon Shin. A scholar is included among the top collaborators of Sangwon Shin 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 Sangwon Shin. Sangwon Shin 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:

16 of 16 papers shown

Chae, Young Kwang, Borislav A. Alexiev, Sangwon Shin, et al.. (2025). Phase II Study of Nivolumab and Ipilimumab for Treatment of Metastatic/Recurrent Adenoid Cystic Carcinoma (ACC) of all Anatomic Sites of Origin and Other Malignant Salivary Gland Tumors. Cancer Medicine. 14(7). e70724–e70724. 2 indexed citations

Imai, Mitsuho, Yoshiaki Nakamura, Sangwon Shin, et al.. (2025). Artificial Intelligence–Powered Human Epidermal Growth Factor Receptor 2 and Tumor Microenvironment Analysis in Human Epidermal Growth Factor Receptor 2–Amplified Metastatic Colorectal Cancer: Exploratory Analysis of Phase II TRIUMPH Trial. JCO Precision Oncology. 9(9). e2400385–e2400385. 2 indexed citations

Kim, Hyung‐Don, Soo Ick Cho, Jinho Shin, et al.. (2025). Artificial intelligence-based prediction of claudin 18.2 expression and immune phenotype to guide treatment decisions in patients with gastric cancer.. Journal of Clinical Oncology. 43(16_suppl). 4047–4047.

Shin, Sangwon, Soo Ick Cho, Wonkyung Jung, et al.. (2024). Artificial intelligence (AI) -powered H&E whole-slide image (WSI) analysis of tertiary lymphoid structure (TLS) to predict response to immunotherapy in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology. 42(16_suppl). 3135–3135. 3 indexed citations

Shin, Sangwon, et al.. (2024). Discontinuation risk from adverse events: immunotherapy alone vs. combined with chemotherapy: a systematic review and network meta-analysis. BMC Cancer. 24(1). 152–152. 3 indexed citations

Shin, Sangwon, et al.. (2024). Seek and Classify: End-to-end Joint Spectrum Segmentation and Classification for Multi-signal Wideband Spectrum Sensing. 1–9.

Kim, Dong‐Hyeon, et al.. (2024). Effectiveness and safety of combined treatment with herbal medicines and palliative chemotherapy for advanced gastric cancer: A systematic review and meta-analysis. Integrative Medicine Research. 14(1). 101098–101098.

Jung, Minsun, Soo Ick Cho, Sangwon Shin, et al.. (2024). Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases. Breast Cancer Research. 26(1). 31–31. 14 indexed citations

Lee, Taebum, Sangwon Shin, Biagio Brattoli, et al.. (2024). Identification of HER2 ultra-low based on an artificial intelligence (AI)-powered HER2 subcellular quantification from HER2 immunohistochemistry images.. Journal of Clinical Oncology. 42(16_suppl). 1115–1115.

10.

Shin, Sangwon, et al.. (2024). StarSPA: Stride-Aware Sparsity Compression for Efficient CNN Acceleration. IEEE Access. 12. 10893–10909. 2 indexed citations

11.

Ahn, Jong Seok, Sangwon Shin, Su‐A Yang, et al.. (2023). Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. Journal of Breast Cancer. 26(5). 405–405. 76 indexed citations

12.

Jung, Minsun, S-W. Kim, Su-Jin Song, et al.. (2023). 366P Artificial intelligence (AI)-powered assessment of complete and intense human epidermal growth factor receptor 2 (HER2)-positive tumor cell proportion in breast cancer: Predicting fluorescence in situ hybridization (FISH) positivity and response to HER2-targeted therapy. Annals of Oncology. 34. S329–S329. 1 indexed citations

13.

Rha, Sun Young, et al.. (2008). A randomized phase II trial of docetaxel plus S-1 versus docetaxel plus cisplatin in advanced gastric cancer as a first-line treatment. Journal of Clinical Oncology. 26(15_suppl). 4534–4534. 3 indexed citations

14.

Shin, Sangwon, et al.. (2008). A randomized open-label study of gefitinib versus docetaxel in patients with advanced/metastatic non-small cell lung cancer (NSCLC) who have previously received platinum-based chemotherapy. Journal of Clinical Oncology. 26(15_suppl). 8025–8025. 18 indexed citations

15.

Rha, Sun Young, et al.. (2007). A phase II study of S-1 monotherapy administered for 2 weeks of a 3-week cycle in advanced gastric cancer patients with poor performance status. 97(4). 458–463. 3 indexed citations

16.

Shin, Sangwon, Hojun Kim, & Sujin Kim. (2003). Obesity from the viewpoint of metabolic rate. Journal of Korean Medicine for Obesity Research. 3(1). 95–105. 5 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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