Zhong-Hua Wang

876 total citations
8 papers, 413 citations indexed

About

Zhong-Hua Wang is a scholar working on Oncology, Cancer Research and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Zhong-Hua Wang has authored 8 papers receiving a total of 413 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Oncology, 4 papers in Cancer Research and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Zhong-Hua Wang's work include Radiomics and Machine Learning in Medical Imaging (2 papers), Medical Imaging Techniques and Applications (2 papers) and Breast Cancer Treatment Studies (2 papers). Zhong-Hua Wang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (2 papers), Medical Imaging Techniques and Applications (2 papers) and Breast Cancer Treatment Studies (2 papers). Zhong-Hua Wang collaborates with scholars based in China, Canada and United States. Zhong-Hua Wang's co-authors include Xi-Chun Hu, Bi-Yun Wang, Jian Zhang, Zhi-Min Shao, Yi Xiao, Jianjun Zou, Yin Liu, Xiaoyu Zhu, Claire F. Verschraegen and Jia-Han Ding and has published in prestigious journals such as Scientific Reports, Cell Research and BMC Cancer.

In The Last Decade

Zhong-Hua Wang

7 papers receiving 408 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Zhong-Hua Wang China	6	179	170	164	116	53	8	413
Caroline Coghlin United Kingdom	6	185 1.0×	134 0.8×	205 1.3×	106 0.9×	27 0.5×	10	447
Connor O’Leary Australia	10	178 1.0×	106 0.6×	147 0.9×	155 1.3×	31 0.6×	29	387
Shuiping Gao China	13	229 1.3×	194 1.1×	263 1.6×	99 0.9×	74 1.4×	28	539
Xinjian Huang China	13	172 1.0×	112 0.7×	190 1.2×	68 0.6×	34 0.6×	25	395
Jinwei Hao Australia	3	197 1.1×	129 0.8×	255 1.6×	92 0.8×	25 0.5×	3	432
Junli Deng China	7	198 1.1×	180 1.1×	332 2.0×	57 0.5×	33 0.6×	8	498
Kosuke Toda Japan	9	181 1.0×	239 1.4×	273 1.7×	49 0.4×	52 1.0×	18	521
Erica C. Nakajima United States	8	248 1.4×	155 0.9×	232 1.4×	202 1.7×	57 1.1×	21	527
Jianguo Lai China	13	159 0.9×	248 1.5×	303 1.8×	126 1.1×	34 0.6×	42	511
Mao Zhang China	8	167 0.9×	112 0.7×	191 1.2×	63 0.5×	54 1.0×	21	411

Countries citing papers authored by Zhong-Hua Wang

Since Specialization

Citations

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

Fields of papers citing papers by Zhong-Hua Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhong-Hua Wang. 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 Zhong-Hua Wang. The network helps show where Zhong-Hua Wang may publish in the future.

Co-authorship network of co-authors of Zhong-Hua Wang

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

All Works

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8 of 8 papers shown

Wang, Yunyi & Zhong-Hua Wang. (2025). Temporal trends in breast cancer treatment patterns and survival: A comprehensive analysis of the SEER database from 1990 to 2017. Current Problems in Surgery. 69. 101795–101795.

Liu, Yin, Yi Xiao, Xin Hu, et al.. (2020). Molecular subtyping and genomic profiling expand precision medicine in refractory metastatic triple-negative breast cancer: the FUTURE trial. Cell Research. 31(2). 178–186. 194 indexed citations

Ma, Ding, Yi‐Rong Liu, Xin Hu, et al.. (2017). Impact of hormone receptor status and distant recurrence-free interval on survival benefits from trastuzumab in HER2-positive metastatic breast cancer. Scientific Reports. 7(1). 1134–1134. 5 indexed citations

Wang, Bi-Yun, Jian Zhang, Si Sun, et al.. (2015). Intermittent high dose proton pump inhibitor enhances the antitumor effects of chemotherapy in metastatic breast cancer. Journal of Experimental & Clinical Cancer Research. 34(1). 109–109. 139 indexed citations

Cao, Jun, Jian Zhang, Bi-Yun Wang, et al.. (2015). The human chemokine receptor CCRL2 suppresses chemotaxis and invasion by blocking CCL2-induced phosphorylation of p38 MAPK in human breast cancer cells. Medical Oncology. 32(11). 254–254. 19 indexed citations

Zhang, Jian, Zhenyu Jia, Joseph Ragaz, et al.. (2013). The maximum standardized uptake value of 18 F-FDG PET scan to determine prognosis of hormone-receptor positive metastatic breast cancer. BMC Cancer. 13(1). 42–42. 29 indexed citations

Zhang, Jian, Zhenyu Jia, Min Zhou, et al.. (2013). The SUVmax for 18F-FDG Correlates With Molecular Subtype and Survival of Previously Untreated Metastatic Breast Cancer. Clinical Nuclear Medicine. 38(4). 256–262. 13 indexed citations

Cao, Bing, Xiao-Yan Zhou, Ye Guo, et al.. (2011). A clinical analysis of primary testicular diffuse large B-cell lymphoma in China. Hematology. 16(5). 291–297. 14 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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