Xiaochen Feng

838 total citations
26 papers, 675 citations indexed

About

Xiaochen Feng is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Xiaochen Feng has authored 26 papers receiving a total of 675 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Oncology, 11 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Molecular Biology. Recurrent topics in Xiaochen Feng's work include Pancreatic and Hepatic Oncology Research (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Neuroendocrine Tumor Research Advances (4 papers). Xiaochen Feng is often cited by papers focused on Pancreatic and Hepatic Oncology Research (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Neuroendocrine Tumor Research Advances (4 papers). Xiaochen Feng collaborates with scholars based in China, United States and Saudi Arabia. Xiaochen Feng's co-authors include Zhenhua Li, Jinchao Zhang, Xinjian Yang, Fei Duan, Xing‐Jie Liang, Dandan Liu, Huifang Liu, Shutao Gao, Kun Ge and Wentong Sun and has published in prestigious journals such as Biomaterials, IEEE Access and BMC Public Health.

In The Last Decade

Xiaochen Feng

25 papers receiving 668 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaochen Feng China 12 331 176 167 145 141 26 675
Lei Xia China 15 307 0.9× 166 0.9× 121 0.7× 33 0.2× 123 0.9× 35 703
Yinhua Jin China 16 353 1.1× 249 1.4× 71 0.4× 31 0.2× 200 1.4× 29 859
Zelong Chen China 17 391 1.2× 271 1.5× 55 0.3× 45 0.3× 71 0.5× 69 850
Xiaoning Tong China 10 386 1.2× 259 1.5× 68 0.4× 89 0.6× 21 0.1× 27 638
Shuaishuai Ding China 12 352 1.1× 258 1.5× 62 0.4× 66 0.5× 20 0.1× 17 618
Chunjuan Jiang China 14 251 0.8× 79 0.4× 189 1.1× 14 0.1× 123 0.9× 28 587
Lizhou Lin China 18 656 2.0× 254 1.4× 202 1.2× 31 0.2× 37 0.3× 24 1.0k
Jingsong Mao China 17 456 1.4× 130 0.7× 47 0.3× 17 0.1× 83 0.6× 45 732

Countries citing papers authored by Xiaochen Feng

Since Specialization
Citations

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

Fields of papers citing papers by Xiaochen Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaochen Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaochen Feng. A scholar is included among the top collaborators of Xiaochen Feng 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 Xiaochen Feng. Xiaochen Feng 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.
Feng, Xiaochen, Yongqiang Wang, Xin Wang, et al.. (2025). Network analysis of the relationships among activities of daily living disability, cognitive impairment, and depression in Chinese older adults. BMC Public Health. 25(1). 4067–4067. 1 indexed citations
2.
Wang, Yongqiang, et al.. (2024). Synergistic pathways for health investment and economic development in China: a fuzzy-set qualitative comparative analysis. Frontiers in Public Health. 12. 1429006–1429006.
3.
4.
Feng, Xiaochen, Jianyong Yuan, Minghao Ruan, et al.. (2023). Prognostic impact of portal area inflammation on intrahepatic cholangiocarcinoma patients without lymph node metastasis. Journal of Gastrointestinal Oncology. 14(5). 2229–2242. 1 indexed citations
5.
Wang, Feng, et al.. (2022). Generating new protein sequences by using dense network and attention mechanism. Mathematical Biosciences & Engineering. 20(2). 4178–4197. 4 indexed citations
6.
Zhang, Hao, Qi Li, Jieyu Yu, et al.. (2022). Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis. European Radiology. 32(9). 6336–6347. 11 indexed citations
7.
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
8.
Lv, Fangfang, Yan Jin, Xiaochen Feng, et al.. (2021). Traceable metallic antigen release for enhanced cancer immunotherapy. Journal of Nanoparticle Research. 23(6). 130–130. 4 indexed citations
9.
Zhang, Hao, Qi Li, Fang Liu, et al.. (2021). Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma. Academic Radiology. 29(4). 523–535. 18 indexed citations
10.
Zhang, Hao, Qi Li, Xu Fang, et al.. (2021). CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma. Frontiers in Oncology. 11. 707288–707288. 14 indexed citations
11.
12.
Bian, Yun, Yan Fang Liu, Hui Jiang, et al.. (2021). Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Abdominal Radiology. 46(10). 4800–4816. 11 indexed citations
13.
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
14.
Li, Jing, Fang Liu, Xu Fang, et al.. (2021). CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis. Academic Radiology. 29(3). 358–366. 20 indexed citations
15.
Liu, Cong, Yun Bian, Fang Liu, et al.. (2021). Preoperative Prediction of G1 and G2/3 Grades in Patients With Nonfunctional Pancreatic Neuroendocrine Tumors Using Multimodality Imaging. Academic Radiology. 29(4). e49–e60. 17 indexed citations
16.
17.
Shao, Chengwei, Xiaochen Feng, Jieyu Yu, et al.. (2021). A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm. Abdominal Radiology. 46(8). 3963–3973. 6 indexed citations
18.
Feng, Xiaochen, et al.. (2021). Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN. Frontiers in Genetics. 12. 709500–709500. 10 indexed citations
19.
Gao, Shutao, Pengli Zheng, Zhenhua Li, et al.. (2018). Biomimetic O2-Evolving metal-organic framework nanoplatform for highly efficient photodynamic therapy against hypoxic tumor. Biomaterials. 178. 83–94. 184 indexed citations
20.
Duan, Fei, Xiaochen Feng, Xinjian Yang, et al.. (2017). A simple and powerful co-delivery system based on pH-responsive metal-organic frameworks for enhanced cancer immunotherapy. Biomaterials. 122. 23–33. 163 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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