Danke Su

766 total citations
45 papers, 517 citations indexed

About

Danke Su is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Otorhinolaryngology. According to data from OpenAlex, Danke Su has authored 45 papers receiving a total of 517 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Biomedical Engineering and 7 papers in Otorhinolaryngology. Recurrent topics in Danke Su's work include MRI in cancer diagnosis (17 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Medical Imaging Techniques and Applications (9 papers). Danke Su is often cited by papers focused on MRI in cancer diagnosis (17 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Medical Imaging Techniques and Applications (9 papers). Danke Su collaborates with scholars based in China and United States. Danke Su's co-authors include Guanqiao Jin, Lidong Liu, Dong Xie, Junjie Liu, Duo Wang, Hai Liao, Kun Zhang, Li Ye, Xiangyang Huang and Chuangye Han and has published in prestigious journals such as Scientific Reports, Chemical Engineering Journal and Frontiers in Immunology.

In The Last Decade

Danke Su

43 papers receiving 508 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Danke Su China 13 217 163 95 90 75 45 517
Guanqiao Jin China 13 203 0.9× 152 0.9× 78 0.8× 63 0.7× 44 0.6× 37 494
M.I. Saunders United Kingdom 12 194 0.9× 46 0.3× 128 1.3× 91 1.0× 21 0.3× 30 489
Stan van Keulen United States 15 175 0.8× 381 2.3× 249 2.6× 109 1.2× 17 0.2× 20 741
Anna Cividalli Italy 13 208 1.0× 391 2.4× 94 1.0× 94 1.0× 26 0.3× 24 667
Adam J. Gomez United States 10 71 0.3× 171 1.0× 114 1.2× 65 0.7× 12 0.2× 22 537
Steven J. de Jongh Netherlands 7 66 0.3× 163 1.0× 109 1.1× 40 0.4× 18 0.2× 9 360
Charlotte E.S. Hoogstins Netherlands 11 123 0.6× 434 2.7× 213 2.2× 154 1.7× 82 1.1× 15 875
John Skarlatos Greece 12 93 0.4× 183 1.1× 337 3.5× 238 2.6× 39 0.5× 20 826
Wei Weng China 10 110 0.5× 85 0.5× 83 0.9× 170 1.9× 80 1.1× 21 474

Countries citing papers authored by Danke Su

Since Specialization
Citations

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

Fields of papers citing papers by Danke Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Danke Su

This figure shows the co-authorship network connecting the top 25 collaborators of Danke Su. A scholar is included among the top collaborators of Danke Su 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 Danke Su. Danke Su 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.
Tian, Xiang, Haiying Li, Xiaowei Wang, & Danke Su. (2025). Melanocortin-1 receptor expression as a predictive factor for postoperative outcomes in melanoma patients: a retrospective study. Frontiers in Immunology. 16. 1570502–1570502. 1 indexed citations
2.
3.
Luo, Xinzhe, Wenxiang Shen, Hai Liao, et al.. (2025). MHAU-Net: a multi-scale hybrid attention U-shaped network for the segmentation of MRI breast tumors. Quantitative Imaging in Medicine and Surgery. 15(5). 4758–4773.
4.
Wang, Fang, et al.. (2024). Attention-guided context asymmetric fusion networks for the liver tumor segmentation of computed tomography images. Quantitative Imaging in Medicine and Surgery. 14(7). 4825–4839. 1 indexed citations
5.
Kang, Wei, et al.. (2022). Contrast-Enhanced Cone-Beam Breast CT: An Analysis of Diagnostic Value in Predicting Breast Lesion With Rim Enhancement Malignancy. Frontiers in Oncology. 12. 868975–868975. 3 indexed citations
6.
You, Pan, Yuchao Yang, Rong Huang, et al.. (2022). Ring finger protein 126 promotes breast cancer metastasis and serves as a potential target to improve the therapeutic sensitivity of ATR inhibitors. Breast Cancer Research. 24(1). 92–92. 6 indexed citations
7.
Huang, Xiangyang, Yinan Ji, Guanqiao Jin, et al.. (2022). A functional liver imaging score for preoperative prediction of liver failure after hepatocellular carcinoma resection. European Radiology. 32(8). 5623–5632. 16 indexed citations
8.
Liu, Lu, Wei Pei, Hai Liao, et al.. (2022). A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma. Frontiers in Oncology. 12. 792535–792535. 4 indexed citations
9.
Ji, Yinan, et al.. (2022). A Dual‐Mode Imaging Nanoparticle Probe Targeting PD‐L1 for Triple‐Negative Breast Cancer. Contrast Media & Molecular Imaging. 2022(1). 2431026–2431026. 4 indexed citations
10.
Pei, Wei, et al.. (2021). Preoperative MR imaging for predicting early recurrence of solitary hepatocellular carcinoma without microvascular invasion. European Journal of Radiology. 138. 109663–109663. 25 indexed citations
11.
Chang, Yan, Xiaobo Chen, Danke Su, et al.. (2021). CT-Based Radiomics Nomogram for Prediction of Progression-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma. Cancer Management and Research. Volume 13. 6911–6923. 14 indexed citations
12.
Liu, Junjie, et al.. (2020). Multi-mode biodegradable tumour-microenvironment sensitive nanoparticles for targeted breast cancer imaging. Nanoscale Research Letters. 15(1). 81–81. 10 indexed citations
13.
Liu, Junjie, et al.. (2020). <p>Dual-Mode Contrast Agents with RGD-Modified Polymer for Tumour-Targeted US/NIRF Imaging</p>. OncoTargets and Therapy. Volume 13. 8919–8929. 7 indexed citations
14.
Li, Wenzhu, Jisheng Xie, Lidong Liu, et al.. (2020). Preoperative normalized iodine concentration derived from spectral CT is correlated with early recurrence of hepatocellular carcinoma after curative resection. European Radiology. 31(4). 1872–1882. 7 indexed citations
15.
Liu, Lijuan, Lu Liu, Yin Li, et al.. (2019). <p>Ultrasmall superparamagnetic nanoparticles targeting E-selectin: synthesis and effects in mice in vitro and in vivo</p>. International Journal of Nanomedicine. Volume 14. 4517–4528. 16 indexed citations
16.
Zhang, Wei, Lijuan Liu, Peng Wang, et al.. (2018). Preoperative computed tomography and serum α-fetoprotein to predict microvascular invasion in hepatocellular carcinoma. Medicine. 97(27). e11402–e11402. 12 indexed citations
17.
Chen, Jia, Guanqiao Jin, Lin‐Hai Yan, & Danke Su. (2017). Association between integrin αvβ3 expression and malignancy lymph node metastasis: A meta-analysis. Biomedical Research-tokyo. 28(7). 2946–2951. 1 indexed citations
18.
Zhang, Wei, Jie Chen, Dong Xie, et al.. (2017). Validated preoperative computed tomography risk estimation for postoperative hepatocellular carcinoma recurrence. World Journal of Gastroenterology. 23(35). 6467–6473. 21 indexed citations
19.
Guan, Ying, et al.. (2017). Intracranial Anatomic Landmarks for Endoscopic Endonasal Transcribriform Approach to Anterior Skull Base. Journal of Craniofacial Surgery. 28(4). 985–987. 3 indexed citations
20.
Hu, Xueying, et al.. (2015). GSTT1 and GSTM1 polymorphisms predict treatment outcome for breast cancer: a systematic review and meta-analysis. Tumor Biology. 37(1). 151–162. 15 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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