Xu Fang

3.9k total citations · 1 hit paper
95 papers, 2.4k citations indexed

About

Xu Fang is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Epidemiology. According to data from OpenAlex, Xu Fang has authored 95 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Oncology, 31 papers in Radiology, Nuclear Medicine and Imaging and 21 papers in Epidemiology. Recurrent topics in Xu Fang's work include Pancreatic and Hepatic Oncology Research (33 papers), Radiomics and Machine Learning in Medical Imaging (25 papers) and Neuroendocrine Tumor Research Advances (12 papers). Xu Fang is often cited by papers focused on Pancreatic and Hepatic Oncology Research (33 papers), Radiomics and Machine Learning in Medical Imaging (25 papers) and Neuroendocrine Tumor Research Advances (12 papers). Xu Fang collaborates with scholars based in China, United States and Switzerland. Xu Fang's co-authors include Zhao Zhao, Jianmin Jiang, Wen Shi, Yun Bian, Jianping Lu, Hui Jiang, Qiong Huang, Kexiang Yan, Kai Cao and Chengwei Shao and has published in prestigious journals such as PLoS ONE, Radiology and Applied Microbiology and Biotechnology.

In The Last Decade

Xu Fang

89 papers receiving 2.3k citations

Hit Papers

Risk Factors and Preventi... 2017 2026 2020 2023 2017 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Risk Factors and Preventions of Breast Cancer
    2017 1.2k citations Zhao Zhao, Xu Fang et al. International Journal of Biological Sciences profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xu Fang China 21 845 686 588 349 281 95 2.4k
Deependra Singh India 11 1.3k 1.5× 993 1.4× 305 0.5× 497 1.4× 250 0.9× 26 3.4k
Jingping Yuan China 26 768 0.9× 1.2k 1.7× 232 0.4× 788 2.3× 415 1.5× 121 3.2k
Peppino Mirabelli Italy 25 344 0.4× 963 1.4× 297 0.5× 349 1.0× 159 0.6× 71 2.2k
Jing Cai China 30 517 0.6× 1.2k 1.7× 247 0.4× 714 2.0× 198 0.7× 99 2.8k
Sukhwinder Kaur United States 31 1.0k 1.2× 1.3k 1.9× 289 0.5× 545 1.6× 421 1.5× 70 2.9k
Zodwa Dlamini South Africa 22 531 0.6× 869 1.3× 152 0.3× 416 1.2× 183 0.7× 110 2.0k
Tina J. Hieken United States 33 1.4k 1.6× 1.3k 1.9× 361 0.6× 1.8k 5.1× 287 1.0× 177 4.4k
Bin Zhou China 30 470 0.6× 995 1.5× 194 0.3× 430 1.2× 582 2.1× 184 2.7k
Sandeep Mathur India 27 926 1.1× 735 1.1× 263 0.4× 519 1.5× 162 0.6× 270 3.0k
Muriel Brackstone Canada 23 1.0k 1.2× 850 1.2× 252 0.4× 1.2k 3.5× 88 0.3× 121 3.0k

Countries citing papers authored by Xu Fang

Since Specialization
Citations

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

Fields of papers citing papers by Xu Fang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xu Fang

This figure shows the co-authorship network connecting the top 25 collaborators of Xu Fang. A scholar is included among the top collaborators of Xu Fang 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 Xu Fang. Xu Fang 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.
Fang, Xu, et al.. (2025). The role of salicylic acid in plant defense responses against biotic stresses. 1(1). 0–0. 5 indexed citations
2.
Zheng, Zhilin, Xu Fang, Jiawen Yao, et al.. (2024). Deep Attention Learning for Pre-operative Lymph Node Metastasis Prediction in Pancreatic Cancer via Multi-object Relationship Modeling. International Journal of Computer Vision. 133(5). 2996–3019.
3.
Yu, Jieyu, Chengwei Chen, Xu Fang, et al.. (2024). Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma. International Journal of Surgery. 110(12). 7656–7670. 4 indexed citations
4.
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6.
Zhang, Qi, Jinran Lin, Zhenghua Zhang, et al.. (2022). MTHFR Polymorphism and Folic Acid Supplementation Influence Serum Homocysteine Levels in Psoriatic Patients Treated with Methotrexate. Journal of Clinical Medicine. 11(15). 4580–4580. 1 indexed citations
7.
Bian, Yun, Zhilin Zheng, Xu Fang, et al.. (2022). Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma. Radiology. 306(1). 160–169. 73 indexed citations
8.
Jiang, Zhongyi, Xu Fang, Weitao Que, et al.. (2021). Recipient C7 rs9292795 genotype and the risk of hepatocellular carcinoma recurrence after orthotopic liver transplantation in a Han Chinese population. BMC Cancer. 21(1). 521–521. 2 indexed citations
9.
10.
Yu, Jieyu, Hao Zhang, Yan Fang Liu, et al.. (2021). Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-contrast MRI. Academic Radiology. 29(9). e167–e177. 8 indexed citations
11.
Huang, Qiong, Xu Fang, Ke Yang, et al.. (2021). The Impact of ANxA6 Gene Polymorphism on the Efficacy of Methotrexate Treatment in Psoriasis Patients. Dermatology. 237(4). 579–587. 9 indexed citations
12.
Fang, Xu, Lili Tian, Yan Li, et al.. (2021). High prevalence of extrapulmonary tuberculosis in dairy farms: Evidence for possible gastrointestinal transmission. PLoS ONE. 16(3). e0249341–e0249341. 11 indexed citations
13.
Zhu, Jie, Ling Han, Ruilai Liu, et al.. (2021). Identification of proteins associated with development of psoriatic arthritis in peripheral blood mononuclear cells: a quantitative iTRAQ-based proteomics study. Journal of Translational Medicine. 19(1). 331–331. 15 indexed citations
14.
Fang, Xu, Fang Liu, Jing Li, et al.. (2021). Computed tomography nomogram to predict a high-risk intraductal papillary mucinous neoplasm of the pancreas. Abdominal Radiology. 46(11). 5218–5228. 10 indexed citations
15.
16.
Yan, Kexiang, Ling Han, Hui Deng, et al.. (2018). The distinct role and regulatory mechanism of IL-17 and IFN-γ in the initiation and development of plaque vs guttate psoriasis. Journal of Dermatological Science. 92(1). 106–113. 19 indexed citations
17.
Zhao, Zhao, et al.. (2017). Risk Factors and Preventions of Breast Cancer. International Journal of Biological Sciences. 13(11). 1387–1397. 1158 indexed citations breakdown →
18.
Zhang, Rong, et al.. (2016). Analysis of epidemiological characteristics of hemorrhagic fever with renal syndrome in Zhejiang Province, China, 2010-2014. Chin J Endemiol. 35(7). 520–524. 1 indexed citations
19.
Fang, Xu, et al.. (2015). Salt-responsive microRNAs in Populus euphratica by deep sequencing.. Zhiwu yanjiu. 35(6). 836–842. 2 indexed citations
20.
Fang, Xu, et al.. (2004). Studies on active expression of nattokinase gene in escherichia coli. The Journal of Microbiology. 24(2). 10–13. 1 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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