Jieyu Yu

482 total citations
26 papers, 329 citations indexed

About

Jieyu Yu is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Epidemiology. According to data from OpenAlex, Jieyu Yu has authored 26 papers receiving a total of 329 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Oncology, 17 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Epidemiology. Recurrent topics in Jieyu Yu's work include Pancreatic and Hepatic Oncology Research (23 papers), Radiomics and Machine Learning in Medical Imaging (16 papers) and Neuroendocrine Tumor Research Advances (6 papers). Jieyu Yu is often cited by papers focused on Pancreatic and Hepatic Oncology Research (23 papers), Radiomics and Machine Learning in Medical Imaging (16 papers) and Neuroendocrine Tumor Research Advances (6 papers). Jieyu Yu collaborates with scholars based in China and United States. Jieyu Yu's co-authors include Chengwei Shao, Yun Bian, Xu Fang, Xiaochen Feng, Hui Jiang, Jianping Lu, Jianping Lu, Jiawen Yao, Le Lü and Hao Zhang and has published in prestigious journals such as Radiology, Advanced Science and Journal of Magnetic Resonance Imaging.

In The Last Decade

Jieyu Yu

24 papers receiving 327 citations

Peers

Jieyu Yu
Sebastian Marschner Germany
Xiaojing He China
Nabil Elshafeey United States
Sara Ahmed United States
Guangxing Wang China
Gaofeng Sun China
Arjun K. Menta United States
Vetri Sudar Jayaprakasam United States
Diem Vuong Switzerland
Sebastian Marschner Germany
Jieyu Yu
Citations per year, relative to Jieyu Yu Jieyu Yu (= 1×) peers Sebastian Marschner

Countries citing papers authored by Jieyu Yu

Since Specialization
Citations

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

Fields of papers citing papers by Jieyu Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jieyu Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Jieyu Yu. A scholar is included among the top collaborators of Jieyu Yu 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 Jieyu Yu. Jieyu Yu 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.
Yuan, Xiaohan, Cheng‐Wei Chen, Zhang Shi, et al.. (2025). Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact. npj Digital Medicine. 8(1). 609–609.
2.
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
3.
4.
Yu, Jieyu, Yan Li, Yuwei Gao, et al.. (2023). Self‐Propelled Enzymatic Nanomotors from Prodrug‐Skeletal Zeolitic Imidazolate Frameworks for Boosting Multimodel Cancer Therapy Efficiency. Advanced Science. 10(22). e2301919–e2301919. 44 indexed citations
5.
Li, Qi, Yukun Chen, Jieyu Yu, et al.. (2023). Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma. Abdominal Radiology. 48(6). 2074–2084. 7 indexed citations
6.
Li, Yan, et al.. (2022). Highly Bright Gold Nanowires Arrays for Sensitive Detection of Urea and Urease. Nanomaterials. 12(22). 4023–4023. 2 indexed citations
7.
Fang, Xu, Qianru Zhang, Fang Liu, et al.. (2022). T2-Weighted Image Radiomics Nomogram to Predict Pancreatic Serous and Mucinous Cystic Neoplasms. Academic Radiology. 30(8). 1562–1571. 3 indexed citations
8.
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
9.
Yu, Jieyu, Jian Zhou, Na Li, et al.. (2022). CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. Abdominal Radiology. 47(8). 2822–2834. 1 indexed citations
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.
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
12.
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
13.
14.
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
15.
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
16.
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
17.
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
18.
19.
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
20.
Chen, Renpin, Zhiming Huang, Xiaohua Ye, et al.. (2014). Analysis of SEMA3B methylation and expression patterns in gastric cancer tissue and cell lines. Oncology Reports. 31(3). 1211–1218. 19 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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