Qing Lv

2.2k citations
76 papers · 1.5k indexed · h-index 22
Topics
Breast Cancer Treatment Studies (14 papers)AI in cancer detection (9 papers)Breast Lesions and Carcinomas (8 papers)

In The Last Decade

Qing Lv

70 papers receiving 1.5k citations

Peers

Qing Lv
Comparison fields: 5 of 132
  • Molecular Biology 404
  • Biomedical Engineering 327
  • Artificial Intelligence 320
  • Radiology, Nuclear Medicine and Imaging 287
  • Surgery 264
Replace Sarun Juengpanich with:
Sarun Juengpanich China
Qianqian Song United States
Xinye Ni China
Shu Wang China
Jialing Liu China
Qiubai Li China
Ke Zhao China
Yu Shi China
Roger Bourne Australia
Yulan Peng China
Qing Lv relative to Sarun Juengpanich China Sarun Juengpanich's profile →
Citations per field
00.5×20×40×63×
Sarun Juengpanich · 1×
Citations per year

Countries citing papers authored by Qing Lv

Since Specialization
Citations

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

Fields of papers citing papers by Qing Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qing Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Qing Lv. A scholar is included among the top collaborators of Qing Lv 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 Qing Lv. Qing Lv 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
#WorkIndexed citations
1 0
2 13
3 2
4 2
5 1
6 28
7 17
8 12
9 5
10
Application of Carbon Nanoparticles in Tracing Lymph Nodes and Locating Tumors in Colorectal Cancer: A Concise Review
1
11 19
12
Enhanced transdermal delivery of lornoxicam by nanostructured lipid carrier gels modified with polyarginine peptide for treatment of carrageenan-induced rat paw edema
3
13 49
14 7
15 4
16 8
17 179
18 4
19 37
20 138

About Qing Lv

Qing Lv is a scholar working on Cancer Research, Fuel Technology and Health Informatics, having authored 76 papers that have together received 1.5k indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (14 papers), AI in cancer detection (9 papers) and Breast Lesions and Carcinomas (8 papers). The work is most often cited by research in Health Informatics (23 citations), Biomaterials (214 citations) and Cancer Research (230 citations). Qing Lv has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Lei Zhang, Yi Zhang, Cato T. Laurencin, Lakshmi S. Nair, Zhenggui Du, Qiuwen Tan, Xiaofeng Qi, Yong Pi, Zizhou Wang and Yi Chen. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

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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