Yuankai Shi

1.6k total citations
32 papers, 705 citations indexed

About

Yuankai Shi is a scholar working on Oncology, Pathology and Forensic Medicine and Molecular Biology. According to data from OpenAlex, Yuankai Shi has authored 32 papers receiving a total of 705 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Oncology, 12 papers in Pathology and Forensic Medicine and 8 papers in Molecular Biology. Recurrent topics in Yuankai Shi's work include Lymphoma Diagnosis and Treatment (9 papers), Lung Cancer Treatments and Mutations (6 papers) and Viral-associated cancers and disorders (5 papers). Yuankai Shi is often cited by papers focused on Lymphoma Diagnosis and Treatment (9 papers), Lung Cancer Treatments and Mutations (6 papers) and Viral-associated cancers and disorders (5 papers). Yuankai Shi collaborates with scholars based in China, United States and Japan. Yuankai Shi's co-authors include Frank B. Hu, Zhiqiang Ning, Xuezhi Hao, Bin Wang, Xianping Lu, Haixiang Cao, Mei Dong, Zhiyun Meng, Lin Lin and Feng-yi Feng and has published in prestigious journals such as The Lancet, Journal of Clinical Oncology and Blood.

In The Last Decade

Yuankai Shi

31 papers receiving 684 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuankai Shi China 12 283 177 154 105 93 32 705
Binbin Cui China 16 267 0.9× 244 1.4× 110 0.7× 49 0.5× 145 1.6× 53 743
Orazia M. Granata Italy 19 234 0.8× 164 0.9× 95 0.6× 197 1.9× 96 1.0× 40 962
Saeeda O. Ahmed Saudi Arabia 13 312 1.1× 140 0.8× 125 0.8× 55 0.5× 113 1.2× 28 620
Jong Sil Lee South Korea 15 353 1.2× 195 1.1× 87 0.6× 42 0.4× 102 1.1× 53 809
Milena Čavić Serbia 15 161 0.6× 189 1.1× 61 0.4× 40 0.4× 100 1.1× 59 682
Jae Hyuck Choi South Korea 15 221 0.8× 171 1.0× 150 1.0× 132 1.3× 215 2.3× 37 913
Chang Pan China 18 450 1.6× 116 0.7× 130 0.8× 43 0.4× 161 1.7× 45 869
Pasqualino Favoriti Italy 7 225 0.8× 262 1.5× 91 0.6× 44 0.4× 152 1.6× 14 694
Huihui Li China 16 164 0.6× 90 0.5× 111 0.7× 35 0.3× 50 0.5× 38 683

Countries citing papers authored by Yuankai Shi

Since Specialization
Citations

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

Fields of papers citing papers by Yuankai Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuankai Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Yuankai Shi. A scholar is included among the top collaborators of Yuankai Shi 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 Yuankai Shi. Yuankai Shi 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.
Lu, Jingyu, et al.. (2024). Proteomic profiling reveals the significance of lipid metabolism in small cell lung cancer recurrence and metastasis. Journal of Translational Medicine. 22(1). 1117–1117. 2 indexed citations
3.
Xie, Tongji, et al.. (2024). Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study. Cancer Immunology Immunotherapy. 74(1). 29–29. 1 indexed citations
4.
5.
Qin, Yan, Xinrui Chen, Sheng Yang, et al.. (2024). Deciphering the Prognostic Significance of MYD88 and CD79B Mutations in Diffuse Large B-Cell Lymphoma: Insights into Treatment Outcomes. Targeted Oncology. 19(3). 383–400. 1 indexed citations
6.
Xu, Jianping, Xiaoyan Liu, Sheng Yang, & Yuankai Shi. (2022). Efficacy, safety, and prognostic factors of apatinib plus platinum doublet chemotherapy in advanced non-small cell lung cancer. Journal of Cancer Research and Therapeutics. 18(5). 1425–1431. 1 indexed citations
7.
Kostakoglu, Lale, Maurizio Martelli, Laurie H. Sehn, et al.. (2021). End-of-treatment PET/CT predicts PFS and OS in DLBCL after first-line treatment: results from GOYA. Blood Advances. 5(5). 1283–1290. 36 indexed citations
8.
Hu, Xingsheng, Xin Zheng, Sheng Yang, et al.. (2020). First-in-human phase I study of BPI-9016M, a dual MET/Axl inhibitor, in patients with non-small cell lung cancer. Journal of Hematology & Oncology. 13(1). 6–6. 15 indexed citations
9.
Chen, Haizhu, Yan Qin, Jianliang Yang, et al.. (2020). Event-free survival at 24 months is a robust surrogate endpoint for long-term survival in pediatric, adolescent, and adult T cell lymphoblastic lymphoma. Annals of Hematology. 99(12). 2847–2857. 3 indexed citations
10.
Chen, Haizhu, Yu Zhou, Xiaohong Han, & Yuankai Shi. (2020). The Changing Landscape of Anti-Lymphoma Drug Clinical Trials in Mainland China in the Past 15 Years (2005-2020): A Systematic Review. SSRN Electronic Journal. 1 indexed citations
11.
Han, Xiaohong, Ning Li, Shuxia Liu, et al.. (2019). A new method of ultra‐performance liquid chromatography/tandem mass spectrometry for determination of chlorogenic acid in human plasma and urine. Rapid Communications in Mass Spectrometry. 34(5). e8603–e8603. 1 indexed citations
12.
Jiang, Shiyu, Sheng Yang, Jianliang Yang, et al.. (2019). Evaluating stress, satisfaction and the associated influencing factors of participants in cancer clinical trials: a cross-sectional study in China. BMJ Open. 9(5). e028589–e028589. 1 indexed citations
13.
Xing, Puyuan, Xiaohong Han, Sha Wang, et al.. (2019). Co‐mutational assessment of circulating tumour DNA (ctDNA) during osimertinib treatment for T790M mutant lung cancer. Journal of Cellular and Molecular Medicine. 23(10). 6812–6821. 11 indexed citations
14.
Sun, Yongkun, Lin Yang, Xuezhi Hao, et al.. (2019). Phase I dose-escalation study of chiauranib, a novel angiogenic, mitotic, and chronic inflammation inhibitor, in patients with advanced solid tumors. Journal of Hematology & Oncology. 12(1). 9–9. 26 indexed citations
15.
16.
Kostakoglu, Lale, Maurizio Martelli, Laurie H. Sehn, et al.. (2017). Baseline PET-Derived Metabolic Tumor Volume Metrics Predict Progression-Free and Overall Survival in DLBCL after First-Line Treatment: Results from the Phase 3 GOYA Study. Blood. 130(Suppl_1). 824–824. 20 indexed citations
17.
Jiang, Shiyu & Yuankai Shi. (2017). [The development of biosimilars in oncology].. PubMed. 39(10). 721–725. 1 indexed citations
18.
Lin, Lin, Haibo Lin, Lin Wang, et al.. (2015). miR-130a regulates macrophage polarization and is associated with non-small cell lung cancer. Oncology Reports. 34(6). 3088–3096. 51 indexed citations
19.
Wang, Jianfei, Yinchen Shen, Shuai Wang, et al.. (2013). Direct sequencing is a reliable assay with good clinical applicability for KRAS mutation testing in colorectal cancer. Cancer Biomarkers. 13(2). 89–97. 13 indexed citations
20.
Dong, Mei, Zhiqiang Ning, Haixiang Cao, et al.. (2012). Phase I study of chidamide (CS055/HBI-8000), a new histone deacetylase inhibitor, in patients with advanced solid tumors and lymphomas. Cancer Chemotherapy and Pharmacology. 69(6). 1413–1422. 90 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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