Yuguo Wei

440 total citations
34 papers, 242 citations indexed

About

Yuguo Wei is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Yuguo Wei has authored 34 papers receiving a total of 242 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Oncology and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Yuguo Wei's work include Radiomics and Machine Learning in Medical Imaging (25 papers), Advanced X-ray and CT Imaging (8 papers) and Pancreatic and Hepatic Oncology Research (6 papers). Yuguo Wei is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (25 papers), Advanced X-ray and CT Imaging (8 papers) and Pancreatic and Hepatic Oncology Research (6 papers). Yuguo Wei collaborates with scholars based in China, Spain and Sweden. Yuguo Wei's co-authors include Cuiyun Wu, Junfa Chen, Yanqing Ma, Jing Liu, Jianfei Fu, Yang Zhang, Junkang Shen, Bin Hu, Qinghua Wang and Lixia Wang and has published in prestigious journals such as Nature Communications, Global Biogeochemical Cycles and World Journal of Gastroenterology.

In The Last Decade

Yuguo Wei

30 papers receiving 240 citations

Peers

Yuguo Wei

RNMI

ONCOL

PRM

HEPAT

Rita Simões Netherlands

Fabian Tollens Germany

Yanqing Ma China

Xiaoming Zhou China

Mohab M. Elmohr United States

Giuseppe Corrias Italy

Hongliang Sun China

Alonso García-Ruiz Spain

Zhijun Geng China

Rita Simões Netherlands

Yuguo Wei

223 ×1.2

RNMI

54 ×0.6

ONCOL

87 ×1.4

PRM

53 ×1.0

HEPAT

60 ×1.2

Citations per year, relative to Yuguo Wei Yuguo Wei (= 1×) peers Rita Simões

Countries citing papers authored by Yuguo Wei

Since Specialization

Citations

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

Fields of papers citing papers by Yuguo Wei

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuguo Wei

This figure shows the co-authorship network connecting the top 25 collaborators of Yuguo Wei. A scholar is included among the top collaborators of Yuguo Wei 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 Yuguo Wei. Yuguo Wei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Zhang, Zihan, et al.. (2025). Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole‐Brain Radiomics: An Unsupervised Machine Learning Study. CNS Neuroscience & Therapeutics. 31(2). e70182–e70182. 1 indexed citations

Ren, Dabin, et al.. (2025). Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram. Frontiers in Oncology. 15. 1513193–1513193.

Lian, Yong, et al.. (2025). Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients. BMC Medical Imaging. 25(1). 227–227. 1 indexed citations

Wei, Yuguo, et al.. (2025). Estimating the rate of acute adverse reactions to non-ionic low-osmolar contrast media: a systematic review and meta-analysis. European Radiology. 35(10). 6240–6249. 1 indexed citations

Wei, Yuguo, et al.. (2024). Value of perfusion parameters from golden-angle radial sparse parallel dynamic contrast-enhanced magnetic resonance imaging in predicting pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Diagnostic and Interventional Radiology. 0(0). 0–0.

Ye, Qin, et al.. (2024). Effect of Diffuse Idiopathic Skeletal Hyperostosis on the Occurrence of Thoracolumbar Vertebral Fragility Fractures at Different Ages. Global Spine Journal. 15(4). 2075–2086.

Liang, Hong, Yanqing Ma, Hang Yuan, et al.. (2024). Comparison of conventional MRI analysis versus MRI-based radiomics to predict the circumferential margin resection involvement of rectal cancer. BMC Gastroenterology. 24(1). 209–209. 1 indexed citations

Mu, Cuicui, Yating Chen, Yuguo Wei, et al.. (2023). Ecosystem CO₂ Exchange and Its Economic Implications in Northern Permafrost Regions in the 21st Century. Global Biogeochemical Cycles. 37(11). 1 indexed citations

Gao, Chen, et al.. (2023). A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer. Heliyon. 9(12). e23242–e23242. 1 indexed citations

10.

Ye, Qin, et al.. (2023). Quantitative evaluation of the infrapatellar fat pad in knee osteoarthritis: MRI-based radiomic signature. BMC Musculoskeletal Disorders. 24(1). 326–326. 12 indexed citations

11.

Feng, Na, et al.. (2023). A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers. Frontiers in Oncology. 12. 1106525–1106525.

12.

Zhang, Yang, et al.. (2023). Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics. Diagnostics. 13(2). 269–269. 9 indexed citations

13.

Wang, Yiran, et al.. (2022). Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma. Frontiers in Oncology. 12. 915871–915871. 9 indexed citations

14.

Wu, Cuiyun, Junfa Chen, Yuqian Fan, et al.. (2022). Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma. Frontiers in Oncology. 12. 943942–943942. 24 indexed citations

15.

Chen, Bingchen, et al.. (2022). The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma. BMC Gastroenterology. 22(1). 463–463. 13 indexed citations

16.

Jiang, Tingting, Fang Wang, Yuguo Wei, et al.. (2022). Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study. Frontiers in Oncology. 12. 975881–975881. 1 indexed citations

17.

Hu, Xi, et al.. (2022). Can the combination of DWI and T2WI radiomics improve the diagnostic efficiency of cervical squamous cell carcinoma?. Magnetic Resonance Imaging. 92. 197–202. 3 indexed citations

18.

Zhang, Guozheng, Hong Yang, Jun Luo, et al.. (2022). A CT-Based Radiomics Nomogram to Predict Complete Ablation of Pulmonary Malignancy: A Multicenter Study. Frontiers in Oncology. 12. 841678–841678. 6 indexed citations

19.

Yu, Peng, et al.. (2022). A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma. Cancer Management and Research. Volume 14. 2409–2418. 12 indexed citations

20.

Liang, Hong, et al.. (2021). How does the pancreatic solid pseudopapillary neoplasm confuse us: Analyzing from the point view of MRI-based radiomics?. Magnetic Resonance Imaging. 85. 38–43. 5 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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