Jianmin Ding

648 total citations
36 papers, 464 citations indexed

About

Jianmin Ding is a scholar working on Hepatology, Epidemiology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jianmin Ding has authored 36 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Hepatology, 20 papers in Epidemiology and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jianmin Ding's work include Hepatocellular Carcinoma Treatment and Prognosis (27 papers), Liver Disease Diagnosis and Treatment (18 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (9 papers). Jianmin Ding is often cited by papers focused on Hepatocellular Carcinoma Treatment and Prognosis (27 papers), Liver Disease Diagnosis and Treatment (18 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (9 papers). Jianmin Ding collaborates with scholars based in China and United States. Jianmin Ding's co-authors include Xiang Jing, Yandong Wang, Yan Zhou, Fengmei Wang, Yijun Wang, Du Zhi, Ji‐Bin Liu, Xinyu Zhong, Hongyu Zhou and Yuting Cao and has published in prestigious journals such as Journal of Clinical Oncology, Chemical Engineering Journal and European Radiology.

In The Last Decade

Jianmin Ding

33 papers receiving 457 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jianmin Ding China 11 298 199 99 83 69 36 464
Zhongyang Shen China 10 264 0.9× 124 0.6× 182 1.8× 44 0.5× 81 1.2× 21 488
Qungang Shan China 13 153 0.5× 106 0.5× 70 0.7× 62 0.7× 141 2.0× 24 432
Maarten C. Jansen Netherlands 9 363 1.2× 140 0.7× 142 1.4× 72 0.9× 49 0.7× 10 520
Alessandra R. Garcia United States 4 356 1.2× 95 0.5× 102 1.0× 51 0.6× 64 0.9× 4 458
Shree Venkat United States 10 206 0.7× 89 0.4× 91 0.9× 53 0.6× 62 0.9× 25 398
Laura Venerandi Italy 9 295 1.0× 190 1.0× 159 1.6× 52 0.6× 64 0.9× 16 578
Jacopo Lera Italy 3 569 1.9× 359 1.8× 144 1.5× 64 0.8× 74 1.1× 4 684
Svetlana Gourevitch Israel 7 211 0.7× 72 0.4× 31 0.3× 72 0.9× 45 0.7× 9 391
Zebin Chen China 12 259 0.9× 77 0.4× 59 0.6× 36 0.4× 181 2.6× 28 485

Countries citing papers authored by Jianmin Ding

Since Specialization
Citations

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

Fields of papers citing papers by Jianmin Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jianmin Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Jianmin Ding. A scholar is included among the top collaborators of Jianmin Ding 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 Jianmin Ding. Jianmin Ding 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.
Yang, Huiyu, et al.. (2025). Three-dimensional Contrast-enhanced Ultrasound for Evaluating the Shape and Size of the Microwave Ablation Zone. Ultrasound in Medicine & Biology. 51(8). 1291–1297.
2.
Zhou, Yan, et al.. (2024). Head-to-head comparison of three different US-based quantitative parameters for hepatic steatosis assessment: a prospective study. Abdominal Radiology. 49(7). 2262–2271. 3 indexed citations
3.
Zhou, Hongyu, Jianmin Ding, Yan Zhou, et al.. (2024). Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning. BMC Medical Imaging. 24(1). 68–68. 3 indexed citations
4.
5.
Ding, Jianmin, Yandong Wang, Hongyu Zhou, et al.. (2024). Dynamic Contrast-Enhanced Ultrasonography Combined With LR-M Classification Criteria for Differentiating Malignant Liver Nodules at High Risk for Hepatocellular Carcinoma. Ultrasound in Medicine & Biology. 50(7). 1028–1033. 2 indexed citations
6.
Zhao, Lin, et al.. (2023). Primary hepatic squamous cell carcinoma: case report and systematic review of the literature. Frontiers in Oncology. 13. 1229936–1229936. 2 indexed citations
7.
Zhou, Yan, Jianmin Ding, Lin Zhao, et al.. (2022). Risk Stratification and Distribution of Hepatocellular Carcinomas in CEUS and CT/MRI LI-RADS: A Meta-Analysis. Frontiers in Oncology. 12. 873913–873913. 10 indexed citations
8.
Zhou, Yan, Jianmin Ding, Xiang Zhang, et al.. (2022). Combination of CT/MRI LI-RADS with CEUS can improve the diagnostic performance for HCCs. European Journal of Radiology. 149. 110199–110199. 7 indexed citations
9.
Zhou, Yan, Lin Zhao, Xiang Zhang, et al.. (2022). CEUS and CT/MRI LI-RADS in Association With Serum Biomarkers for Differentiation of Combined Hepatocellular-Cholangiocarcinoma From Hepatocellular Carcinoma. Frontiers in Oncology. 12. 897090–897090. 10 indexed citations
10.
Zhou, Yan, Yandong Wang, Fengmei Wang, et al.. (2020). Additional Diagnostic Value of Fusion Imaging of CEUS and First CEUS of Invisible Hepatic Lesions ≤2 cm. Journal of Ultrasound in Medicine. 40(6). 1173–1181. 3 indexed citations
11.
12.
Ding, Jianmin, Xiang Zhang, Chen Chen, et al.. (2020). Contrast-enhanced ultrasound LI-RADS 2017: comparison with CT/MRI LI-RADS. European Radiology. 31(2). 847–854. 28 indexed citations
13.
Zhou, Yan, Jianmin Ding, Yijun Wang, et al.. (2020). Predicting the survival rate of patients with hepatocellular carcinoma after thermal ablation by nomograms. Annals of Translational Medicine. 8(18). 1159–1159. 7 indexed citations
14.
Jing, Xiang, Yan Zhou, Jianmin Ding, et al.. (2020). The Learning Curve for Thermal Ablation of Liver Cancers: 4,363-Session Experience for a Single Central in 18 Years. Frontiers in Oncology. 10. 540239–540239. 7 indexed citations
15.
16.
Zhou, Yan, Xiaolin Xu, Jianmin Ding, et al.. (2018). Dynamic changes of T-cell subsets and their relation with tumor recurrence after microwave ablation in patients with hepatocellular carcinoma. Journal of Cancer Research and Therapeutics. 14(1). 40–45. 39 indexed citations
17.
Ding, Jianmin, Yan Zhou, Yandong Wang, et al.. (2017). Percutaneous microwave ablation of exophytic tumours in hepatocellular carcinoma patients: Safe or not?. Liver International. 37(9). 1365–1372. 16 indexed citations
18.
Ding, Jianmin & Xiang Jing. (2017). Clinical Value of Contrast-Enhanced Ultrasound in Differential Diagnosis of Early Hepatocellular Carcinoma and Dysplastic Nodules. Ultrasound in Medicine & Biology. 43. S46–S47. 1 indexed citations
19.
Wang, Yandong, Xiang Jing, & Jianmin Ding. (2015). Clinical value of dynamic 3-dimensional contrast-enhanced ultrasound imaging for the assessment of hepatocellular carcinoma ablation. Clinical Imaging. 40(3). 402–406. 12 indexed citations
20.
Ding, Jianmin, Xiang Jing, Ji‐Bin Liu, et al.. (2013). Comparison of two different thermal techniques for the treatment of hepatocellular carcinoma. European Journal of Radiology. 82(9). 1379–1384. 103 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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