Zhenyu Shu

990 total citations
50 papers, 692 citations indexed

About

Zhenyu Shu is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Zhenyu Shu has authored 50 papers receiving a total of 692 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Oncology and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Zhenyu Shu's work include Radiomics and Machine Learning in Medical Imaging (37 papers), MRI in cancer diagnosis (15 papers) and Colorectal Cancer Surgical Treatments (8 papers). Zhenyu Shu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (37 papers), MRI in cancer diagnosis (15 papers) and Colorectal Cancer Surgical Treatments (8 papers). Zhenyu Shu collaborates with scholars based in China, Spain and United States. Zhenyu Shu's co-authors include Peipei Pang, Xiangyang Gong, Yuyun Xu, Dewang Mao, Sijia Cui, Songhua Fang, Qin Ye, Minming Zhang, Xuehua Wen and Liping Fu and has published in prestigious journals such as Scientific Reports, Neuroscience and Magnetic Resonance in Medicine.

In The Last Decade

Zhenyu Shu

46 papers receiving 682 citations

Peers

Zhenyu Shu

RNMI

ONCOL

PRM

Karoline Skogen Norway

Jiangfen Wu China

Jung Youn Kim South Korea

Donghyun Kim South Korea

Wansheng Long China

Yaqiong Ge China

Guangyao Wu Netherlands

Fides R. Schwartz United States

Hwan-ho Cho South Korea

Benjamin Wildman‐Tobriner United States

Karoline Skogen Norway

Zhenyu Shu

488 ×0.9

RNMI

113 ×0.7

ONCOL

85 ×0.5

149 ×1.3

PRM

45 ×0.5

Citations per year, relative to Zhenyu Shu Zhenyu Shu (= 1×) peers Karoline Skogen

Countries citing papers authored by Zhenyu Shu

Since Specialization

Citations

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

Fields of papers citing papers by Zhenyu Shu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenyu Shu

This figure shows the co-authorship network connecting the top 25 collaborators of Zhenyu Shu. A scholar is included among the top collaborators of Zhenyu Shu 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 Zhenyu Shu. Zhenyu Shu 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

Hou, Jie, Fan Yang, Yang Gao, et al.. (2025). MR imaging of breast cancer: Interpretable radiomics analysis to assess treatment response and survival prognosis after neoadjuvant therapy. International Journal of Cancer. 157(8). 1723–1733.

Hou, Tao, et al.. (2025). Cerebral blood flow based on 3D-ASL technology in the early detection of mild cognitive impairment in type 2 diabetic patients: a pilot study. Frontiers in Endocrinology. 16. 1576998–1576998.

Xu, Yuyun, et al.. (2024). Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study. CNS Neuroscience & Therapeutics. 30(6). e14789–e14789. 5 indexed citations

Xing, Xiaowei, et al.. (2024). A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer. Scientific Reports. 14(1). 11760–11760. 6 indexed citations

Shu, Zhenyu, Hui Jin, Lu Han, et al.. (2024). Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms. Journal of Cancer Research and Clinical Oncology. 150(3). 147–147. 5 indexed citations

Zhong, Xia, et al.. (2023). Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning. Cancer Imaging. 23(1). 88–88. 4 indexed citations

Shu, Zhenyu, Xiao-Li Zheng, Meng Ma, et al.. (2023). A novel CT-responsive hydrogel for the construction of an organ simulation phantom for the repeatability and stability study of radiomic features. Journal of Materials Chemistry B. 11(46). 11073–11081. 2 indexed citations

Jin, Hui, Fanfan Zhao, Yuting Yan, et al.. (2023). Is coronary artery calcium an independent risk factor for white matter hyperintensity?. BMC Neurology. 23(1). 313–313. 5 indexed citations

Cheng, Xuefei, et al.. (2023). Prediction of Mild Cognitive Impairment Progression to Alzheimer’s Disease Based on Diffusion Tensor Imaging-Derived Diffusion Parameters: Construction and Validation of a Nomogram. European Neurology. 86(6). 408–417. 2 indexed citations

10.

Hou, Jie, Hui Jin, Yongsheng Zhang, et al.. (2023). Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study. Frontiers in Cardiovascular Medicine. 10. 1282768–1282768. 2 indexed citations

11.

Xu, Yuyun, et al.. (2021). The Nomogram of MRI‐based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study. Journal of Magnetic Resonance Imaging. 54(2). 571–583. 17 indexed citations

12.

Shu, Zhenyu, et al.. (2021). Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. European Radiology. 32(2). 1002–1013. 35 indexed citations

13.

Cui, Sijia, et al.. (2021). Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study. Cancer Imaging. 21(1). 26–26. 32 indexed citations

14.

Shu, Zhenyu, et al.. (2020). Radiomics from magnetic resonance imaging may be used to predict the progression of white matter hyperintensities and identify associated risk factors. European Radiology. 30(6). 3046–3058. 20 indexed citations

15.

Fu, Liping, et al.. (2020). A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection. Journal of Thoracic Imaging. 35(6). 361–368. 33 indexed citations

16.

Chen, Shujun, et al.. (2020). Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients. Frontiers in Oncology. 10. 1410–1410. 43 indexed citations

17.

Shu, Zhenyu, Peipei Pang, Xiao Wu, et al.. (2020). An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI. Frontiers in Aging Neuroscience. 12. 548616–548616. 20 indexed citations

18.

Shu, Zhenyu, et al.. (2019). A radiomic nomogram based on T2WI for predicting synchronous liver metastasis of rectal cancer. Zhonghua fangshexian yixue zazhi. 53(3). 205–211. 1 indexed citations

19.

Shu, Zhenyu, Songhua Fang, Qin Ye, et al.. (2019). Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdominal Radiology. 44(11). 3775–3784. 37 indexed citations

20.

Chen, Zhonghua, et al.. (2018). Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study. Frontiers in Aging Neuroscience. 10. 393–393. 37 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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